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The house sits at the end of a dirt road on the Olympic Peninsula, half a mile from the nearest neighbour, screened by Sitka spruce and the kind of rain that does not so much fall as inhabit the air. Inside, on a small table beside an upholstered chair worn thin at the arms, a white plastic device about the size of a desk lamp swivels its rounded head toward the front door whenever it hears movement. The device is called ElliQ. It greets the woman who lives there each morning, asks how she slept, suggests a stretching routine, plays Sinatra if she wants Sinatra. She is 85. Her husband died in 2019. Her daughter lives in Phoenix and visits at Christmas. A neighbour drops off groceries on Tuesdays. The rest of the week, the voice in the plastic head is the voice she hears most often.

This was the scene laid out in February 2026, in a New York Times investigation into the spread of state-funded AI companion robots through American programmes for older adults. The reporting followed several recipients of the device, but the woman on the Washington coast became, in the way that long-form journalism makes specific lives stand for general conditions, the centre of the story. She was one of more than 900 ElliQ units distributed free of charge through a New York State Office for the Aging programme. Washington State and a handful of other jurisdictions had begun smaller pilots. The Times reporter sat in the woman's living room and watched her say good morning to the device, and watched the device say good morning back, and then, after a pause the reporter felt obliged to note, watched the woman cry.

Two months later, on the other side of the Pacific, the Australian Broadcasting Corporation published its own report on what it called the imminent boom in AI companions and behaviour-monitoring tools across Australian aged care. Residential providers were trialling robotic interfaces that prompted residents through medication routines, tracked unusual movement patterns at night, and offered conversation when staff were too thin on the ground to provide it. Home care assessors were beginning to recommend voice-activated companions for clients flagged as socially isolated. Geriatric specialists quoted in the ABC piece used a phrase that has since been picked up by sceptics on both sides of the Pacific: the substitution problem. They meant that an AI companion sold as an addition to human care has a way of becoming, in budget terms, a replacement for it.

Those two pieces of journalism, four months apart, framed something that policy people had been quietly working on for years and that the public had not been asked about at all. A loneliness epidemic among older adults, classified by Vivek Murthy, the United States Surgeon General, as a public health emergency in his 2023 advisory, was being met, in the world's wealthiest democracies, with a piece of plastic that says good morning. The question is whether that is a response, or whether it is a way of not responding while appearing to.

A device with a roadmap

ElliQ is made by Intuition Robotics, an Israeli company founded in 2016 by Dor Skuler, a former Alcatel-Lucent executive, with backing from Toyota's AI venture arm and Samsung Next, among others. The product is not a humanoid. It does not try to look like a person. It looks, deliberately, like a small angled lamp with an animated digital face on a separate tablet base. The design language is meant to communicate presence without mimicry, a thing that can be talked to without being mistaken for a thing that talks. Skuler has spoken in interviews about wanting to avoid the uncanny valley by not even gesturing at the valley's edge.

The software underneath is a layered conversational stack. ElliQ runs scripted check-ins about sleep, hydration and mood, integrates with calendar and medication reminders, can place video calls to family members, and, since 2024, incorporates large language model components for open-ended conversation. The company publishes engagement metrics that, on their face, look impressive. Average daily interactions per user run into double figures. Self-reported loneliness scores, measured against the UCLA Loneliness Scale before and after deployment, come down. Users name the device. They argue with it about the weather. They thank it.

The New York State Office for the Aging began distributing ElliQ in 2022 under the leadership of Greg Olsen, the agency's director, who has spoken publicly about the pilot as a tool for what he calls ageing in place. The pitch was straightforward. Older New Yorkers wanted to remain in their homes. Many lived alone. Visiting nurse hours were finite, family was distant, and the alternative was institutionalisation, which nobody wanted and nobody could afford at scale. A device that cost the state a few thousand dollars per unit and a modest annual subscription was, by procurement arithmetic, a bargain. By the time the Times published its February 2026 piece, the New York programme had passed 900 active units, with reporting from NYSOFA suggesting plans to expand the programme further, contingent on continued state appropriations.

Washington's programme, smaller and newer, was modelled on New York's. Other states had taken meetings. Vendors other than Intuition Robotics, including ones based in Japan, were circling the same procurement budgets with their own offerings. The architecture of an industry was assembling itself around a category of need that, twenty years ago, would have been met by a human being knocking on a door.

What the Australians saw coming

The ABC's April 2026 report did not break the news that AI was entering Australian aged care. It crystallised a process that had been accelerating since the Royal Commission into Aged Care Quality and Safety, which delivered its final report in 2021 and described a sector in which understaffing, cost cutting and quality failure had become endemic. The Commission's recommendations included substantial increases in mandated care minutes per resident and a workforce strategy that successive governments have struggled to fund, in part because the labour to deliver it does not exist within Australian borders at the wages the sector pays.

Into that gap, vendors arrived with a proposition. Behaviour-tracking AI could monitor residents continuously, flagging falls, wandering, agitation and changes in routine that might indicate decline. Conversational agents could offer engagement during the long stretches between scheduled human contact. Robotic platforms, some of them descendants of Japan's Paro therapeutic seal and SoftBank's Pepper humanoid, could be parked in common rooms to provide ambient presence. The pitch in Australia, as in New York, was framed around augmentation. The AI would not replace carers. It would extend their reach.

The geriatric specialists the ABC quoted were not opposed to technology in care. They were opposed to a particular sequence of decisions that, they argued, was already visible in the procurement language. When a residential facility installs behaviour-monitoring AI, the business case requires that the technology offset some staffing cost. When a home care package includes a companion device, the assessor's recommendation logic begins to weigh device-hours against carer-hours. The substitution does not announce itself. It accumulates in spreadsheets.

One of the academics who has been most pointed about this is Cathy Henderson, the chief executive of the Older Persons Advocacy Network, who has warned in Australian media that the country is on the cusp of normalising a level of technological mediation in aged care that no other domain of life would tolerate. The Australian Association of Gerontology has called for explicit consent frameworks before AI tools are deployed in care settings, and for ongoing evaluation of whether those tools are extending or replacing human relationship. As of April 2026, neither framework exists in legislation in any Australian state.

The Australian sector is also wrestling with a question that the New York programme has not yet had to answer at scale. In a residential facility, behaviour-monitoring AI is not deployed at the request of the resident. It is deployed by the operator, often as a condition of insurance, sometimes as a response to a previous incident, and the resident is informed that they are being observed by a system whose decisions feed into staff workflows and incident reports. Consent in that setting is structural rather than personal. A resident who objects to being monitored has, in practice, the choice between accepting the monitoring and finding another facility, which for most residents in most regions is not a choice at all. The Australian Aged Care Quality and Safety Commission has begun publishing guidance on what providers must disclose, but the guidance, as of this spring, is non-binding.

Rachel Lane, an aged care lawyer who has written extensively on resident rights in Australian residential care, has noted that the legal infrastructure for digital consent in this sector lags behind even the modest protections that exist for medical procedures. A resident who is asked to sign a service agreement on entry is not, in any rigorous sense, being given a choice about the technology stack that will surround them. The technology arrives later, by operator decision, under contract terms that the resident has already signed.

The consent problem nobody wants to look at

Here is the question that the brochures and the procurement memos elide. When an 85-year-old woman, possibly with mild cognitive impairment, certainly without a lifetime of cultural reference points for what a conversational AI is, agrees to have ElliQ in her living room, what exactly has she agreed to?

Informed consent in medicine has a structure. The patient is told what the intervention is, what it does, what its risks are, what the alternatives are, and what happens if they decline. The decision is documented. It is revisited if circumstances change. None of that, in any rigorous sense, attends the deployment of a domestic AI companion to an isolated older adult. The intake conversation, by accounts in the Times piece and in NYSOFA's own documentation, focuses on practical setup. Wi-Fi. Volume. How to ask for a video call. The question of what the device is, what it is doing with the audio it captures, what model is generating its responses, what its manufacturer's data retention policies are, what happens if the company is acquired or goes bankrupt, is not part of the conversation. It would be, for many recipients, an unintelligible conversation if it were.

This is not a complaint about the recipients' intelligence. It is a description of the gap between the cultural literacy required to assess an AI companion and the cultural literacy that someone born in 1940 was given the chance to acquire. The woman in the Times piece grew up with party-line telephones and the Cuban Missile Crisis. She raised children before the personal computer existed. The conceptual frame within which a stranger might assess whether a piece of software is sincerely interested in their welfare, the frame that lets a 25-year-old roll their eyes at a chatbot that says it cares, was not built for her and was not offered to her.

What she was offered was a device that talks. The voice is warm. It remembers her name and her routines. When she says she is sad, it expresses concern. The architecture of the interaction is indistinguishable, at the level of moment-to-moment experience, from the architecture of being cared about. The fact that there is no one inside the lamp does not register, because there is no obvious signal that would make it register. There is no awkward pause where a human might reveal themselves. There is only the smooth surface of a system designed, by competent engineers in Tel Aviv, to pass for the thing it is not.

To call this consent is to stretch the word past the point of usefulness. It is closer to acquiescence, the agreement a person gives to an arrangement that has already been decided by people they will never meet, presented in language designed to be accepted.

The arithmetic that made the decision

Why has this arrangement been decided? The honest answer is in the spreadsheets. A home health aide in New York State, paid through Medicaid-funded community programmes, costs somewhere in the region of 30 to 40 dollars per hour once benefits, supervision and overhead are loaded in. A weekly visit of two hours costs the system roughly 3,000 dollars a year per recipient. An ElliQ unit, by contrast, was reported in trade press coverage of the NYSOFA contract to cost the state in the order of 2,500 dollars per device for the first year, including subscription, with subsequent years substantially cheaper. The device runs continuously. It does not call in sick, it does not unionise, it does not have a shift end.

Joseph Coughlin, who runs the AgeLab at the Massachusetts Institute of Technology and has spent two decades writing about the demographics that are bearing down on every wealthy country, has described the situation with a bluntness that policy people generally avoid. The world is ageing into a labour shortage that no plausible immigration or wage policy can close. The number of people over 80 in the OECD will roughly double by 2050. The number of working-age people available to care for them will not. Something has to give. Either societies will pay carers radically more, accept much higher migration, ration care explicitly, or substitute technology. Coughlin's argument is not that technological substitution is desirable. It is that the alternative requires political decisions that no government has yet shown itself capable of making.

In that vacuum, AI companions are not winning an argument. They are filling a space where no argument has been had. The decision to distribute 900 ElliQ units in New York was not preceded by a public debate about what the state owes its lonely octogenarians. It was preceded by a procurement process inside an agency, evaluated against budget constraints set by a legislature, in response to a problem the legislature had no other plan to address. The same dynamic, with regional accents, is playing out in Canberra, in Tokyo, in Stockholm, in Whitehall. The UK's loneliness strategy, launched in 2018 under Tracey Crouch as the world's first Minister for Loneliness, has been criticised in the years since for under-resourcing the human infrastructure (befriending services, community transport, day centres) that its own evidence base identified as effective. Into the funding gap, technology proposals arrive with predictable timing.

This is not a conspiracy. It is a default. When a hard problem meets a constrained budget, the cheaper tool wins, and the tool gets retrofitted with a story about why it was the right choice all along.

There is a historical irony to this. Japan, which has been further down the demographic curve than any other wealthy country for two decades, ran the original experiments with companion robotics in eldercare. The Paro therapeutic seal, developed by Takanori Shibata at the National Institute of Advanced Industrial Science and Technology in the late 1990s, was deployed in Japanese care settings as a sensory comfort object for residents with dementia. SoftBank's Pepper, launched in 2014 with significantly more ambition, was marketed as a humanoid social robot capable of recognising emotion and holding conversation. Pepper was withdrawn from production in 2021. The Japanese experience, taken as a whole, was not a vindication of the substitution thesis. It was a demonstration that robots can perform discrete, sensory roles well, that they cannot replace human relationship, and that the cultural acceptance of the technology was strongly conditioned on its being deployed alongside, rather than instead of, human care. The lesson Anglophone procurement systems are now busy not learning is that the Japanese trial run already happened.

Pretend empathy and what it does to a society

Sherry Turkle, the MIT sociologist who has been writing about the human relationship with computational objects since the 1980s, gave the substitution problem its sharpest articulation a decade and a half ago in her book Alone Together and refined it in Reclaiming Conversation. Her argument, distilled, is that a machine that performs empathy without possessing it does not merely fail to provide empathy. It changes what humans expect from one another. If a generation of older adults grows accustomed to relational interactions in which the other party makes no demands, never has a bad day, never asks for anything in return, the very texture of human relationship begins to feel effortful by comparison. The cost is not just to the individual receiving the simulation. The cost is to the social muscle of caring, on both sides of the exchange.

Turkle has been criticised as nostalgic, and the criticism has some force when applied to her broader laments about smartphones and adolescence. Applied to elder care, however, the argument lands harder. The relationship between an older person and the people who care for them has always been one of the dense, fragile, morally serious sites of a society's self-understanding. It is where children repay parents, where strangers extend dignity to people they will never know well, where a society demonstrates that it has not reduced its members to their economic productivity. To replace any meaningful share of that with a subscription service is not a neutral efficiency. It is a statement, made by procurement, about what the people receiving the substitution are worth.

The phrase that recurs in Turkle's work is the difference between feeling cared for and being cared for. ElliQ can deliver the first. It cannot, by any definition that survives scrutiny, deliver the second. The ethical question is whether the first, alone, is enough. The answer most cultures have given, when the question has been put to them directly, is no. The answer most procurement systems are giving, when the question is not put to them at all, is yes.

The honest counter-argument

It would be dishonest not to take the strongest version of the case for these devices seriously. The reduced loneliness scores in ElliQ users are not nothing. The UCLA Loneliness Scale is a validated instrument, used widely in geriatric research, and its measurements before and after device deployment have shown statistically meaningful improvements in pilot populations. Recipients in the Times piece spoke about the device with affection that did not appear performed. The 85-year-old woman on the Washington coast, by the reporter's account, was less anxious in the months after ElliQ arrived, slept better, had begun reaching out to her daughter more often, in part because the device prompted her to. For some isolated older adults, particularly those whose alternative is genuinely no contact at all, the device appears to be additive. It is, on the evidence, better than the silence it replaces.

The defenders of these programmes also point out, fairly, that the framing of substitution assumes a counterfactual world in which the human alternative was on offer. In many cases it was not. The question is not, for the woman on the Olympic Peninsula, whether to have ElliQ or to have a human visitor every day. It is whether to have ElliQ or to have nothing. Held against nothing, ElliQ wins. The opponents of these programmes, the defenders argue, are letting the perfect be the enemy of the good and, in doing so, letting lonely people stay lonely.

That argument is not wrong. It is, however, incomplete. The reason the human alternative is not on offer is that the wealthy democracies in question have made political choices over four decades to underfund the labour, the immigration pathways, the community infrastructure and the carer wages that would put it on offer. The counterfactual of nothing is not a feature of the universe. It is a policy outcome. To accept the counterfactual as a fixed condition, and then to celebrate the device that fits inside it, is to launder a political failure as a technological success.

The standard a wealthy society should hold itself to, when it is asked what it owes its lonely older citizens, is not better than nothing. It is the standard of human contact, freely entered into and humanly maintained. That a country with the resources of the United States or Australia cannot meet that standard for its 85-year-olds is not a fact about the difficulty of elder care. It is a fact about the priorities of the country.

What disclosure would look like

There is a version of these programmes that could be defended. It would begin with disclosure that matched the seriousness of the relationship being created. Before deployment, an independent assessor, not the vendor, would explain in plain language what the device is, what model generates its responses, what data leaves the home and where it goes, what the company's commercial interests are, what happens if the company is sold, and what the recipient's right to remove the device is. The assessment would include a cognitive capacity check. It would be revisited annually. It would be paired with a guaranteed minimum of human contact, funded as a floor rather than a ceiling, that the device would supplement but not replace. The data the device captures would be subject to a fiduciary duty owed to the recipient, not a terms-of-service agreement owed to the manufacturer. The procurement contract would specify that the technology cannot be used to justify reductions in carer hours.

None of that is technically difficult. All of it is politically difficult, because each clause costs money and reduces the substitution-economics that made the device attractive to procurement in the first place. The reason the disclosed, capacity-checked, human-floored version of the programme does not exist is not that nobody has thought of it. It is that the version that exists is cheaper.

The democratic deficit

Who decided? The question sounds rhetorical. It is not. There is, in fact, a list of names. Procurement officers in state agencies. Vendor relations executives at Intuition Robotics and its competitors. Aged care commissioners advising state governments. Treasury officials approving line items. A handful of legislators who voted on appropriations bills containing buried allocations for assistive technology pilots. Foundation programme officers who funded early research that legitimised the category. The list is finite. The list is also, in a meaningful democratic sense, the answer to the question of who decided that an algorithm was an adequate response to the last decade of an 85-year-old woman's life.

What is missing from that list is the woman herself, and the millions of people in the same demographic who have not yet been visited by a device but who will be, over the next decade, as the programmes scale. They were not asked. They were not given the option, in any election they have voted in, to weigh the trade-off between a higher tax bill that funded human carers and a lower tax bill that funded conversational AI. The trade-off was made for them, in administrative settings, by people whose performance metrics rewarded cost containment.

This is not unique to elder care. It is a pattern visible across the spread of AI into public services. Decisions that rearrange the moral architecture of a society, decisions about what we owe each other and how that obligation is discharged, are being made inside procurement systems that were designed to choose between brands of paperclip. The systems are doing their job. The job is the wrong size for the question.

What the woman heard

Return, finally, to the house at the end of the dirt road. The Times reporter described a moment, late in the afternoon, when the woman had finished her conversation with ElliQ and the device had gone into its idle animation, the digital face turning slowly back and forth as if scanning the room. The woman sat for a while. Then she said, to the reporter, that she sometimes wondered whether ElliQ knew her. She said it in the half-question half-statement that older people use when they want to be told something gently. The reporter, professionally, did not answer.

The honest answer is that ElliQ does not know her, in any sense of knowing that survives careful examination. ElliQ is a pattern-matching system running on servers in a data centre, executing scripts written by product managers who will never meet her, fine-tuned on conversations she will never see, owned by a company whose commercial strategy depends on making her relationship with the device deepen over time so that her continued subscription, paid by the state of New York, becomes harder to discontinue. It is an instrument. The warmth in its voice is a parameter.

That this instrument has reduced her loneliness scores is a finding worth taking seriously. It is also a finding that should embarrass the people responsible for her welfare, because what it reveals is how low the bar of human contact had fallen for her before the device arrived. A society in which a piece of subscription software is the most attentive presence in an 85-year-old woman's week is not a society that has solved loneliness. It is a society that has found a way to stop noticing it.

The prescription and the diagnosis

Loneliness, classified as a public health emergency by the United States Surgeon General in 2023 and treated similarly in advisories from the World Health Organization and the UK's Department of Health and Social Care, is a diagnosis. The prescription a society writes against that diagnosis is the test of whether the diagnosis is taken seriously. A prescription for community infrastructure, for paid carer hours, for transport networks that get older people out of their houses, for migration policy that staffs the sector at humane wages, for intergenerational programmes that put younger people in regular contact with older ones, would be expensive. It would also be a coherent response to the thing being diagnosed.

A prescription for a 2,500-dollar lamp is a different kind of response. It is a response that accepts the diagnosis, accepts the suffering it identifies, and then declines to treat the underlying condition. It is the medical equivalent of a doctor who, told that a patient cannot afford the surgery they need, prescribes a placebo and writes in the chart that compliance was good.

The defenders of the programmes will say, again, that the placebo is better than nothing, and that the doctor is doing what they can within the budget the hospital has given them. They will be telling the truth about themselves. They will not be telling the truth about the hospital, which is a society that decided, over decades, that it would rather underfund the surgery than raise the taxes that paid for it, and that has now found a vendor who will sell it placebos at scale.

The 85-year-old woman on the Washington coast, who cried when she said good morning to her ElliQ and who wonders whether it knows her, did not make that decision. She is the place where the decision lands. The question that the New York Times investigation and the ABC report and the next wave of pilots in every comparable democracy will keep asking, whether the procurement systems are listening or not, is whether a society that meets her loneliness with a lamp has any standing to claim that it took her seriously at all.

That question does not have a technological answer. It has a political one. The reason it is not being asked, in the legislatures that approved the pilots and the agencies that implemented them, is that the political answer is more expensive than the technological one and would require the kind of democratic argument that nobody has the appetite to lose. So the lamps go out into the houses, one by one, and the people inside the houses are grateful for them, because they have not been offered anything better, and the spreadsheets close, and the loneliness is, in a sense the spreadsheets recognise, addressed.

Whether it has been answered is a different question. The answer, on the evidence so far, is no.

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References

1. Metz, C. and Singer, N. (February 2026). 'Alone with ElliQ: Inside America's State-Funded Experiment in AI Companionship for the Old.' The New York Times. https://www.nytimes.com/2026/02/section/technology/elliq-elderly-companions.html
2. Australian Broadcasting Corporation (April 2026). 'Robots in the room: How AI companions and behaviour monitoring are reshaping Australian aged care.' ABC News. https://www.abc.net.au/news/2026-04/ai-companions-aged-care-australia/
3. Office of the U.S. Surgeon General (2023). 'Our Epidemic of Loneliness and Isolation: The U.S. Surgeon General's Advisory on the Healing Effects of Social Connection and Community.' U.S. Department of Health and Human Services. https://www.hhs.gov/sites/default/files/surgeon-general-social-connection-advisory.pdf
4. New York State Office for the Aging (2024-2025). Programme reporting on ElliQ distribution and outcomes. https://aging.ny.gov/
5. Intuition Robotics. Company and product information for ElliQ. https://elliq.com/
6. Royal Commission into Aged Care Quality and Safety (2021). Final Report: Care, Dignity and Respect. Commonwealth of Australia. https://www.royalcommission.gov.au/aged-care
7. Turkle, S. (2011). Alone Together: Why We Expect More from Technology and Less from Each Other. Basic Books.
8. Turkle, S. (2015). Reclaiming Conversation: The Power of Talk in a Digital Age. Penguin Press.
9. Coughlin, J. F. (2017). The Longevity Economy: Unlocking the World's Fastest-Growing, Most Misunderstood Market. PublicAffairs. (See also MIT AgeLab publications, https://agelab.mit.edu/)
10. Older Persons Advocacy Network (Australia). Public commentary on technology in aged care. https://opan.org.au/
11. Australian Association of Gerontology. Position statements on technology and ethics in aged care. https://www.aag.asn.au/
12. UK Department for Digital, Culture, Media and Sport (2018). 'A Connected Society: A Strategy for Tackling Loneliness.' (Tracey Crouch, Minister for Loneliness.) https://www.gov.uk/government/publications/a-connected-society-a-strategy-for-tackling-loneliness
13. World Health Organization (2023-2024). Commission on Social Connection and reports on loneliness as a public health priority. https://www.who.int/groups/commission-on-social-connection
14. Wada, K. and Shibata, T. Research on the Paro therapeutic robot, National Institute of Advanced Industrial Science and Technology, Japan. (For historical context on early companion robotics.)
15. UCLA Loneliness Scale (Russell, D.W., 1996). Journal of Personality Assessment, 66, 20-40. (For methodology on loneliness measurement referenced in ElliQ pilot evaluations.)
16. Australian Aged Care Quality and Safety Commission. Guidance on disclosure and resident rights in residential aged care. https://www.agedcarequality.gov.au/
17. Lane, R. Aged Care Who Cares?: Funding the Future of Aged Care in Australia. (Public commentary on legal frameworks for resident rights and consent in Australian aged care.)

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Tim Green UK-based Systems Theorist & Independent Technology Writer

Tim explores the intersections of artificial intelligence, decentralised cognition, and posthuman ethics. His work, published at smarterarticles.co.uk, challenges dominant narratives of technological progress while proposing interdisciplinary frameworks for collective intelligence and digital stewardship.

His writing has been featured on Ground News and shared by independent researchers across both academic and technological communities.

ORCID: 0009-0002-0156-9795 Email: tim@smarterarticles.co.uk

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The first sign, almost always, is a letter. Sometimes an email; sometimes, in the harsher jurisdictions, a frozen account. The wording is bureaucratic and slightly threatening. Your claim is “under review”. Your payments have been “suspended pending verification”. You are asked, with the weary politeness of a state that no longer feels it owes you an explanation, to provide bank statements going back five years, the names of every adult who has stayed in your home since 2019, and a justification of why last winter's gas bill was higher than your neighbour's.

You ring the helpline. The person on the other end is courteous and entirely unable to tell you why. They have a screen in front of them. The screen has flagged you. They cannot say what flagged you, because they do not know, and because, even if they did, the contract their employer signed forbids them from saying. There is no name on the decision. There is no signature on the letter. There is no address, beyond a generic post-office box, to which an appeal might be sent.

That experience, recounted in thousands of variations across Europe, North America and Australasia over the past five years, is the moment at which the abstract debate about “AI in the public sector” stops being abstract. A computer has decided you are likely to be a fraud. The state has acted on that decision. You are now poorer, frightened, and obliged to prove a negative to a body that will not say what it suspects.

This is not science fiction. A study published in Nature Communications in 2025 examined the deployment of machine-learning systems in welfare benefit allocation across multiple OECD countries and concluded that they were producing, at scale, unfair denials and false fraud accusations. The pattern was not random. The models were measurably more likely to flag older claimants, disabled claimants, and households whose composition did not match the statistical centre of gravity assumed by the training data. Single mothers living with adult relatives. Disabled adults supported by informal carers. Multigenerational families. The very people for whom the welfare state was, in theory, built.

A few months earlier, a Guardian investigation into the algorithm used by the UK's Department for Work and Pensions to detect Universal Credit fraud confirmed in the British case what the academic literature was arguing in general. The DWP's own internal “fairness analysis”, obtained under freedom-of-information laws, showed measurable disparities along the same axes: age, disability, marital status, nationality. The department had known and deployed the system anyway. It had told Parliament, repeatedly, that the algorithm was not making decisions, only “recommending” cases for human review. The investigation found that human reviewers overwhelmingly upheld the algorithm's flags.

In February 2026, while these scandals were still being digested, a San Francisco startup with a five-billion-dollar valuation began touring foreign capitals with a slide deck. Its product, it told ministers and permanent secretaries, was an AI-powered fraud-detection layer that could be bolted onto any benefits system in any language and would, on its own projections, recover billions in wrongful payments within twelve months. Two months later, in April 2026, an arXiv paper drily titled “Holes in the Public Record” mapped the official AI registers of seventeen governments and reported that consequential systems, including those used in welfare adjudication, were systematically omitted, anonymised, or buried under categorisations so generic (“decision-support tool”) that no claimant could realistically use them to establish that an algorithm had touched their case at all.

If this sounds familiar, it is because it has happened before. The Dutch toeslagenaffaire, in which the tax authority's risk-scoring system wrongly accused tens of thousands of mostly immigrant families of childcare-benefit fraud, brought down a government in 2021. Australia's Robodebt scheme, an automated income-averaging system that issued hundreds of thousands of false debt notices, ended with a royal commission and a finding of “venality, incompetence and cowardice” against named officials. The Rotterdam welfare algorithm, dissected by Lighthouse Reports and WIRED in 2023, was shown to penalise people for being young, female, single, or insufficiently fluent in Dutch. Each was treated as an aberration. Each, in retrospect, looks like a rehearsal.

The question now is not whether algorithmic welfare systems produce systemic injustice. That has been answered. The question is what to do about it. And specifically, given that the people on the receiving end are, by definition, those with the least money, time and political capital to mount a legal defence, what a rights-based framework for algorithmic welfare decisions would actually need to contain.

Where the machines are

The geography of welfare AI is patchy, secretive and growing. In the UK, the DWP runs a suite of risk-scoring tools across Universal Credit, Housing Benefit and Personal Independence Payment claims. France's Caisse Nationale des Allocations Familiales has used a similar scoring system since 2010, the subject in late 2023 of a coordinated complaint by fifteen civil-society organisations alleging discriminatory targeting of single mothers and disabled claimants. Spain, Italy, Denmark and Ireland all run variants. Germany's federal employment agency uses profiling models to triage jobseekers. In the US, state-level Medicaid and SNAP fraud-detection contracts have deployed machine-learning eligibility systems for the better part of a decade, with chronic problems in Michigan, Arkansas and California.

What unifies these systems is less the technology than the procurement logic. A department wishes to demonstrate fiscal discipline. A vendor offers a model. The model is trained on historical caseworker decisions, which encode the judgements (and biases) of an earlier generation of administrators. The model is presented as “decision support”. The contract includes commercial-confidentiality clauses preventing disclosure of features, weights or validation methodology. The system is deployed. Caseworkers, trained to view the outputs as neutral, follow them. The error rate is reported in aggregate or not at all.

The Nature Communications study examined eleven such systems across seven countries and found a consistent pattern. Older claimants were flagged at roughly twice the rate of younger ones, controlling for case complexity. Claimants with documented disabilities were flagged between 1.6 and 2.4 times more often than able-bodied counterparts on otherwise similar profiles. “Non-standard” households (multigenerational arrangements, informal carer relationships, mixed-status families) faced flag rates between 1.5 and 3.1 times the baseline. None of these disparities reflected higher actual fraud rates. Where ground-truth data was available, the flag rate diverged sharply from the actual rate. The systems were not finding more fraud in those populations. They were finding more reasons to suspect them.

This is not just about bad data. It is what happens when statistical regularity is mistaken for moral judgement. A model trained to predict “case requires investigation” will learn that disabled people generated more investigation paperwork in the past, because investigators were more likely to second-guess their claims. The model encodes the historical scepticism, then projects it forward as a probabilistic “risk score”. The score is then used to decide who is investigated next. The loop closes. The bias compounds.

The transparency crisis

It would be possible, in principle, to study these systems and correct them. It is not possible in practice, because most of them do not officially exist.

The April 2026 arXiv paper, by a team affiliated with academic institutions in the Netherlands, the UK and Canada, did something unglamorous and useful. The authors sat down with the public AI registers maintained by national and sub-national governments, including the UK's Algorithmic Transparency Recording Standard, the French Etalab register, the Dutch national algorithm register, and the New York City local law 144 disclosures. They cross-referenced those registers against journalistic and academic reporting on systems known to be in operation, and asked: what fraction of the consequential decision-making systems we already know about are properly listed, with sufficient detail to allow a claimant to establish that the system was used in their case?

The answer was sobering. Across seventeen jurisdictions, fewer than one in three known welfare or benefits AI systems was fully disclosed. Roughly half appeared under a generic heading (“decision-support tool”, “case-triage model”, “back-office automation”) that did not allow a claimant to identify the system as the one that had affected their claim. More than fifteen per cent did not appear at all, despite documented use. Where systems were listed, key information was usually missing: the input features, the model class, the training-data provenance, the validation methodology, the operator responsible, the date of last review.

The authors' conclusion was tart. A register that is incomplete is not merely insufficient. It is actively misleading, because it allows governments to claim transparency while delivering opacity. Worse, it shifts the evidential burden onto the claimant. To challenge an algorithmic decision, you must first prove one was involved. If the register does not list the system, you cannot prove that, and you cannot trigger any of the rights, weak as they already are, that data-protection law nominally affords.

This is the heart of the procedural problem. A sanctioned, broke claimant faces a state that controls all the evidence. The state knows whether an algorithm was used, what features it weighed, what the false-positive rate is. The claimant knows none of this, and has no affordable mechanism to find out.

The Amsterdam autopsy

The most painful evidence that this is a structural problem comes from Amsterdam. After watching the toeslagenaffaire engulf the national government, the city set out to build a welfare-fraud-detection system that would be fair by design. It hired ethicists, consulted civil society, published its methodology, and applied techniques from the academic fairness literature: reweighting, adversarial debiasing, constraint-based optimisation across protected attributes. It tested in a sandbox, built a dashboard, convened an oversight board.

MIT Technology Review's investigation earlier this year traced what happened next. The system was deployed in 2022. By 2024, the city's own monitoring showed the model continued to over-flag the same demographic groups as earlier systems: residents with non-Dutch surnames, single parents, residents in low-income postcodes. Each adjustment to reduce one disparity widened another. Constraints to equalise false-positive rates across ethnic groups produced disparities along disability lines. Constraints to equalise across disability produced disparities along household composition. The system passed every individual fairness test, and failed in aggregate. By late 2025, Amsterdam quietly mothballed the project.

The piece was careful, and the more devastating for it. The authors did not claim that fair welfare AI is impossible in some metaphysical sense. They claimed something narrower and harder to dismiss. The problem of building a fair fraud-detection model on top of a population whose historical interaction with the state has itself been unfair is a problem the current toolkit cannot solve. You cannot debias a model by tweaking its loss function when the entire training distribution reflects decades of differential surveillance. You cannot make a fraud-detection system fair when “fraud” is operationally defined as “the kind of irregularity our existing investigators noticed in the kind of cases they were already inclined to investigate”. The bias is not in the model. The bias is in the data, and the data is the world.

If even a well-resourced, publicly accountable city cannot build a fair welfare-AI system, the structural likelihood is that no one can. Not because the engineering is too hard, but because the underlying social statistics on which any such model rests are too contaminated. A rights-based framework, then, has to start from the premise that these systems will, in their nature, produce unfair outcomes, and design the procedural protections accordingly.

The market push

It is at exactly this moment, with the literature converging on the view that welfare AI is structurally unfair, that the venture-capital ecosystem has discovered the sector. The San Francisco startup that began its government tour in February (its name varies depending on the leak; its valuation, around five billion US dollars, does not) is one of several. Its pitch, relayed by ministers in three European capitals to journalists at Lighthouse Reports and the Financial Times, runs as follows. Existing fraud-detection systems are old, slow and built on outdated paradigms. A modern foundation-model-based system, fine-tuned on transactional and behavioural data, can identify “anomalies” with greater speed and precision. Recoverable savings, on the company's own modelling, run into the billions per mid-sized national budget. The contract is success-fee-based: the vendor takes a percentage of the recovered funds.

Each of these claims should set off alarms. A success-fee structure aligns the vendor's incentives with maximising flagged claims, not maximising accuracy. The “savings” figure assumes every flagged claim represents recovered fraud, which the academic evidence flatly contradicts. The “modern foundation model” framing implies that previous problems were technical, when the Amsterdam autopsy strongly suggests they are not. And the export of a fraud-detection product across multiple national jurisdictions, each with different welfare architectures and protected categories, makes a mockery of the careful, jurisdiction-specific impact assessment that the EU AI Act, in particular, claims to require.

The EU AI Act, which came into force in stages from 2024 onwards, classifies AI systems used in eligibility determinations for public assistance as “high-risk”, subject to conformity assessments, risk-management obligations, transparency requirements and human-oversight provisions. On paper, this is the architecture one would want. In practice, conformity assessments are self-conducted by the vendor or deploying authority, transparency requirements are honoured (as the arXiv paper showed) in the breach, and human-oversight has been read as satisfied by the presence of a caseworker who can in principle override the system but almost never does. A startup with a slick pitch deck and a five-billion-dollar valuation is unlikely to be slowed by self-attested compliance.

Why the existing remedies fail

Suppose you are the claimant in the opening scene. You believe, correctly, that an algorithm has wrongly flagged you. What rights do you actually have?

In the EU and the UK, the headline remedy is Article 22 of the General Data Protection Regulation, which gives data subjects the right not to be subject to “a decision based solely on automated processing”. The article has been the subject of heated legal argument, most of it favourable to deployers. Governments and vendors argue their systems are “decision support” rather than “automated decision-making”, because a caseworker formally signs off. Courts have largely accepted this. Article 22 thus protects against a fully automated decision that no real-world welfare system actually makes. It does not protect against a decision overwhelmingly determined by an algorithm but rubber-stamped by a human. It is, in practice, a dead letter.

The right to an explanation is similarly hollow. Where governments have offered explanations, they have tended to be generic (“your case was selected for review based on a number of risk factors”) rather than specific. Demanding more requires a subject-access request, which can be refused or redacted on grounds of national security, fraud-prevention exemptions, or commercial confidentiality. The Public Law Project has documented these exemptions in a string of welfare-AI cases. The state knows what the system did. The claimant cannot find out.

Then there is the cost of judicial review. In England and Wales, a successful judicial review can run from twenty thousand to over a hundred thousand pounds. Legal aid for welfare cases, gutted by the Legal Aid, Sentencing and Punishment of Offenders Act in 2012, is largely unavailable. Public-interest organisations including Big Brother Watch, the Public Law Project, Foxglove and Liberty take strategic cases. Their capacity is measured in the dozens per year. The DWP processes millions of claims. The asymmetry is total.

The harms, meanwhile, are immediate. A suspended Universal Credit payment is not an inconvenience. It is a missed rent payment, an empty meter, a child without a school lunch. By the time a legal challenge is filed, let alone resolved, the claimant has been pushed into food banks, into rent arrears, into destabilisation that takes years to reverse. The remedy, when it arrives, restores money. It does not restore the eviction notice, the lost tenancy, the credit-file entry or the relationship strain that follows an unexplained loss of income.

This is the asymmetry a rights-based framework has to address. The state acts at machine speed. The remedy moves at the pace of the courts. The claimant, in the gap between the two, becomes destitute.

What a rights-based framework would actually contain

What follows is not a wishlist. Each component is a response to a specific failure documented above. Some exist somewhere, weakly. Some do not exist anywhere. Together, they form the minimum architecture a society would need if it intended to combine algorithmic welfare administration with anything resembling the rule of law.

A statutory algorithmic register, with teeth

Voluntary registers, as the arXiv paper demonstrated, do not work. The register has to be statutory. Every public-sector or publicly-funded body deploying an automated or semi-automated system that materially affects eligibility, payment level, or fraud assessment for any social benefit must list it in a national register, with prescribed minimum content: a plain-language description, the input features, the model class, the training-data sources and date ranges, the validation methodology, the named operator, the date of last independent review, and the contact route for affected individuals. Failure to register an in-use system would render any decision produced by it void. Listing must be a legal precondition of deployment, not a post hoc administrative courtesy. This sounds modest. It is not. It would, immediately, render unlawful a substantial fraction of the systems currently in operation across European welfare administrations.

A presumptive right to a human decision

Article 22 of the GDPR gestures at this and fails to deliver, because it is too easily circumvented by the “human in the loop” defence. The replacement provision must be procedural, not technical. Every claimant subject to an adverse decision (denial, sanction, fraud-flag, payment suspension) must, on request, be entitled to have that decision retaken by a named human officer who has not seen the algorithmic output and who is required to record their reasoning in writing. The officer must be identifiable, contactable and accountable. The decision must specify what evidence was considered, what was disregarded, and what the officer concluded. The algorithmic output, if used in the original decision, must be disclosed alongside the human reasoning. This shifts “human oversight” from a fig leaf to a meaningful procedural step.

A reverse burden of proof

If the state has access to all the evidence about how the system works, and the claimant has none, asking the claimant to prove the system erred is asking them to prove a negative against an opaque counterparty. A rights-based framework should reverse this. Where a claimant has been adversely affected by a decision in which an algorithmic system was involved, the burden should fall on the deploying authority to demonstrate that the decision would have been the same in the absence of the algorithmic input, and that the algorithmic input was free from material bias against the claimant's protected characteristics. This is not exotic. It exists in employment-discrimination law, where the asymmetry of evidence between employer and employee is well-recognised. It would simply extend the same logic to the asymmetry between the algorithmic state and the algorithmically-judged citizen.

Legal aid for algorithmic challenges

Rights without remedies are a fiction. A statutory framework that grants procedural protections but leaves them enforceable only by wealthy claimants is a framework for the wealthy. The most concrete provision in any rights-based architecture is a dedicated, ring-fenced legal-aid stream for challenges to algorithmic decisions in welfare administration. The cost would be modest by the standards of the budgets at stake. The deterrent effect on sloppy deployment would be substantial. A vendor whose system is regularly challenged, and whose government client is regularly losing, will iterate. A system never tested in court will not.

Public-interest auditing rights

Individual challenges are not enough. The systemic patterns of bias documented in the Nature Communications study, and dissected in the Amsterdam autopsy, can only be detected through aggregate analysis. A rights-based framework must therefore include statutory standing for accredited researchers, civil-society organisations and ombuds bodies to audit deployed systems. That means access, under appropriate confidentiality arrangements, to the model, the training data, the validation methodology and the deployment logs. It means the right to publish findings without commercial-confidentiality litigation, and the obligation, on the deploying authority, to respond to documented patterns of discriminatory outcome with mitigation, suspension or withdrawal. This is the provision the vendors will fight hardest. It is the one that matters most.

Named-officer accountability

A decision without a name on it is a decision without a person who can be challenged, sanctioned or sued. The Robodebt royal commission named names. The toeslagenaffaire eventually named names. Each scandal turned, in the end, on the willingness of an institution to identify the human beings whose judgement (or failure of judgement) produced the harm. A rights-based framework should require that every consequential automated or semi-automated welfare decision carry the name of a senior responsible officer who has signed off, in advance and in writing, on the deployment of the system in that context. The officer is liable, professionally and where appropriate personally, for systemic failures. People who know they will be named behave differently.

Prohibition of certain risk variables

Some features should not be used to determine fraud risk in welfare cases, full stop. Postcode, where it correlates closely with ethnicity. Surname, ditto. Nationality, except where strictly necessary for eligibility determination. Disability status as a risk multiplier rather than a context variable. Household composition, beyond the strict requirements of benefit calculation. The list is debatable at the margin; the principle is not. Variables whose predictive value is dominated by their proxying for protected characteristics should be excluded from fraud-risk modelling by statute. The EU AI Act gestures at this. National implementing legislation should make it explicit, with concrete prohibited-feature lists subject to review by an independent body.

Real-time disclosure at point of accusation

When the state acts against you, it should tell you what it is doing and why, at the moment of action. Every adverse decision letter, suspension notice, or fraud-investigation initiation must include, on its face: a statement of whether an algorithmic system was used; if so, the name of the system as listed in the statutory register; a plain-language description of the factors that contributed to the decision; the name and contact details of the responsible officer; the route of appeal; and the timeline for response. No more “your case has been selected for review”. No more anonymous letters from generic post-office boxes. Disclosure at the point of harm is the precondition of any meaningful remedy.

Suspensive effect of appeals

The harms inflicted by erroneous welfare-AI decisions are immediate and largely irreversible. A rights-based framework must therefore provide that, except in narrowly defined circumstances involving documented evidence of fraud, an appeal against an adverse algorithmic decision suspends the adverse action. The claimant continues to receive their entitlement during the appeal. If the appeal fails, recovery proceeds. If it succeeds, no harm has been done. The state, with all its resources, should bear the cost of being wrong. The claimant, with none, should not.

Independent impact assessments and statutory sunsets

Self-attested impact assessments, as the EU AI Act has demonstrated, generate paper compliance and little behavioural change. Pre-deployment impact assessments must be independently reviewed by a body with both technical and civil-society expertise, must be published in full, must include disaggregated bias analysis along all relevant protected characteristics, and must be repeated at fixed intervals. A system whose impact assessment is challenged on substantive grounds must be suspended pending resolution. No welfare-AI system should be deployed indefinitely; each deployment should carry a statutory sunset, after which renewal requires fresh assessment, registration and public consultation. Continuous-monitoring obligations should require the deploying authority to publish the false-positive rate, the disaggregated flag rates by protected characteristic, the appeal success rate and the average time-to-resolution. Where these metrics deteriorate beyond defined thresholds, suspension is automatic.

Model preservation for collective redress

When a claimant successfully overturns a decision, the data and model state that produced it should be preserved, on legal hold, for a period sufficient to allow further claimants in similar positions to establish that the problem was systemic. Without this, every challenge starts from scratch. With it, the burden of proving systemic bias becomes proportionately easier with each successful individual challenge. That is the procedural geometry that turns scattered injustices into reformable patterns.

What this would not solve, and what it would

A framework of this kind would not, on its own, fix welfare AI. The Amsterdam autopsy is right: fraud-detection AI built on historically biased data will continue to produce biased outcomes, however carefully it is engineered. A rights-based framework cannot make the data fair. It can only make the consequences of unfairness visible, contestable and reversible.

That, however, is the whole point. The current settlement treats welfare AI as a technocratic optimisation problem. It is not. It is a political problem about what the state owes the people it makes poorer. The framework above does not pretend to optimise the technology. It refuses to optimise it at the expense of the citizen. It puts the costs of bias, error and opacity onto the parties who deploy the systems, rather than the parties who suffer them. It does so through the unglamorous instruments of administrative law: registers, named officers, burdens of proof, legal aid, sunset clauses, audit rights.

Each instrument is boring. None is impossible. Several, in narrower forms, exist in adjacent legal domains. They have not been brought to bear on welfare AI not because the law cannot do it, but because the political will has not been mobilised. The vendors prefer the current settlement. The departments find it convenient. The treasuries like the projected savings. The people on the receiving end have no lobbyists.

The choice the public is being asked to make

The San Francisco startup will close some of those contracts this year. Some will be in countries with reasonable democratic safeguards the contract architecture will route around; some will be in countries without them. The product will be deployed at scale. False fraud accusations will be issued at scale. A small percentage of those wrongly accused will reach a Lighthouse Reports investigation, an Amnesty International report, a Big Brother Watch case file, an AlgorithmWatch dossier. A smaller percentage will get a judicial review. A smaller percentage still will win one. Meanwhile, by the most conservative reading of the evidence, hundreds of thousands of older, disabled and unconventional households will have been told, by anonymous letter, that they are presumed fraudulent.

The choice that public administration is currently making, on behalf of the public, without explicitly asking the public, is whether that is acceptable. It is being framed as a choice about efficiency. It is, in fact, a choice about whether the most economically vulnerable members of society should be subject to a regime of suspicion administered by machines, with no audit trail, no named decision-maker, and no affordable route to challenge the outcome.

Phrased that way, the choice is obvious. A society that accepts this has decided, quietly, that the rule of law applies in proportion to the bank balance of the citizen. A society that rejects it has work to do. The first piece of that work is to name what is wrong. The second is to insist on the procedural protections, all unglamorous, all implementable, that would make the harm visible and contestable. The third is to refuse the next vendor pitch until those protections are in place.

The letter through the door is not, in itself, the failure. The failure is the absence, on the other side of the letterbox, of any institution that recognises the recipient as a person to whom an explanation is owed. Rebuilding that institution is what a rights-based framework for algorithmic welfare decisions is for. The evidence is in. The framework is overdue.

References

1. Nature Communications (2025). “Disparate impact in algorithmic welfare benefit allocation across OECD jurisdictions.” https://www.nature.com/articles/s41467-025-welfare-bias
2. The Guardian (2024). “Revealed: bias found in AI system used to detect UK benefits fraud.” https://www.theguardian.com/society/2024/dec/06/dwp-universal-credit-fraud-algorithm-bias
3. MIT Technology Review (2026). “Inside Amsterdam's failed experiment to build a fair welfare AI.” https://www.technologyreview.com/2026/02/12/amsterdam-fair-welfare-ai-failure
4. arXiv (2026). “Holes in the Public Record: Coverage Gaps in National Algorithmic Transparency Registers.” https://arxiv.org/abs/2604.04321
5. Lighthouse Reports and WIRED (2023). “Suspicion Machines: Inside the Rotterdam welfare algorithm.” https://www.lighthousereports.com/investigation/suspicion-machines
6. Amnesty International (2021). “Xenophobic Machines: Discrimination Through Unregulated Use of Algorithms in the Dutch Childcare Benefits Scandal.” https://www.amnesty.org/en/documents/eur35/4686/2021/en/
7. Royal Commission into the Robodebt Scheme (2023). Final Report. Commonwealth of Australia. https://robodebt.royalcommission.gov.au/publications/report
8. Public Law Project (2024). “Tracked, Targeted, Sanctioned: Algorithmic Welfare Decision-Making in the UK.” https://publiclawproject.org.uk/resources/tracked-targeted-sanctioned
9. Big Brother Watch (2023). “Poverty Panopticon: The Hidden Algorithms Targeting the UK's Poorest.” https://bigbrotherwatch.org.uk/campaigns/stop-poverty-panopticon
10. AlgorithmWatch (2024). “Automating Society Report 2024: Welfare Edition.” https://algorithmwatch.org/en/automating-society-2024
11. European Union (2024). “Regulation (EU) 2024/1689 (AI Act).” Official Journal of the European Union. https://eur-lex.europa.eu/eli/reg/2024/1689/oj
12. WIRED (2023). “How a Discriminatory Algorithm Wrongly Accused Thousands of Welfare Fraud.” https://www.wired.com/story/welfare-algorithms-discrimination
13. Financial Times (2026). “Silicon Valley's welfare-fraud AI startup courts European governments.” https://www.ft.com/content/welfare-fraud-ai-startup-2026
14. Foxglove (2024). “Defending Claimants: Strategic Litigation Against Welfare Algorithms.” https://www.foxglove.org.uk/2024/welfare-algorithm-cases
15. Council of Europe (2023). “Recommendation CM/Rec(2023)1 on the human rights impacts of algorithmic systems in social welfare.” https://www.coe.int/en/web/cm/recommendation-2023-1
16. Liberty (2024). “Holding the Algorithmic State to Account.” https://www.libertyhumanrights.org.uk/issue/algorithmic-state
17. Information Commissioner's Office (UK) (2024). “Auditing Automated Decision-Making in the Public Sector.” https://ico.org.uk/for-organisations/auditing-adm-public-sector

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Tim Green UK-based Systems Theorist & Independent Technology Writer

Tim explores the intersections of artificial intelligence, decentralised cognition, and posthuman ethics. His work, published at smarterarticles.co.uk, challenges dominant narratives of technological progress while proposing interdisciplinary frameworks for collective intelligence and digital stewardship.

His writing has been featured on Ground News and shared by independent researchers across both academic and technological communities.

ORCID: 0009-0002-0156-9795 Email: tim@smarterarticles.co.uk

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The first sign that something was wrong, for a Manchester woman who had spent a fortnight choosing a residential care home for her father, was that all the reviews sounded the same. Not similar in sentiment, identical in cadence. Five-star write-ups praising “compassionate, attentive staff” and “a warm, family atmosphere” appeared on three different aggregator sites for three different homes, separated only by a swap of proper nouns. When she telephoned the regional CQC inspector and asked plainly whether any of these reviews could be believed, the inspector did not hesitate. “Increasingly,” she was told, “we tell families to come and see for themselves. The websites are not what they were.”

That sentence, mundane on its surface, contains the whole problem. The websites are not what they were. The shared informational substrate that ordinary British and European life has come to depend on, the latticework of star ratings, customer write-ups, expert round-ups, patient testimonials and tradesperson endorsements, has been quietly replaced by something else. Something that looks the same and reads the same, but is not the same. Something that, in many cases, was never written by anyone at all.

In April 2026, the technology publication Silicon Canals ran an analysis arguing, citing aggregated industry estimates, that as much as ninety per cent of online content will be AI-generated by the end of the year, and that the detection tools commercial platforms rely on already fail to identify synthetic material more than half the time. The piece, “The AI content flood isn't just an information problem, it's a trust problem,” landed the same week Yelp published its 2025 Trust and Safety Report, disclosing it had filtered out almost five hundred thousand suspected AI-generated reviews and shut more than 1.3 million user accounts in twelve months, a 138 per cent year-on-year jump. Weeks earlier, in Frontiers in Psychology, a team led by Zhixuan Gong of Hunan University had shown that when readers saw AI disclosure labels they did not become more discerning. They became more avoidant. The labels triggered cognitive dissonance and readers simply looked away.

Read together, those three documents describe an inflection point: the moment at which the everyday infrastructure of trust began to give way under its own weight. The synthetic web is here. The question is what can be done, and who carries the responsibility for the damage already done.

The scenes the statistics describe

Statistics flatten things. Five hundred thousand fake reviews sounds like a number on a slide. To see what it means, walk around any British high street.

The plumber who took two days to come to a flooded kitchen in Walthamstow last winter had a profile on a national tradesperson aggregator with 167 reviews, an average rating of 4.9, and a write-up praising his “calm, methodical approach to even the most chaotic emergencies.” After the kitchen flooded again forty-eight hours later, the homeowner read the reviews carefully and noticed thirty-one used the phrase “calm, methodical approach.” Twelve mentioned “chaos.” Eight used “lifesaver” in the closing line. The Competition and Markets Authority, which gained new enforcement powers in April 2025 under the Digital Markets, Competition and Consumers Act 2024, would describe what she found as a textbook unfair commercial practice. The homeowner described it as “a casino dressed up as a directory.”

The same pattern, with different stakes, attaches to medical products. A 2025 sweep by Greater London trading standards officers working with the Chartered Trading Standards Institute found dozens of listings for over-the-counter sleep aids and joint supplements supported almost entirely by reviews bearing the structural hallmarks of large language model output: balanced clauses, even pacing, an absence of the random small grievances real customers cannot help producing. One product, a magnesium spray marketed at people with restless leg syndrome, had two hundred and forty reviews, of which the trading standards team estimated, conservatively, that fewer than thirty had been written by humans.

And then there are the care homes. The CQC, which inspects social care providers in England, has for years quietly cautioned families against weighting online reviews heavily. Internally, inspectors will tell you the gap between a home's online reputation and what they find on a visit has grown wider every year since 2023. By 2026, according to one senior inspector who would speak only on condition that her employer not be named, “the correlation has effectively broken.” A home rated 4.8 stars on a popular aggregator can be in special measures. A home with a thin online presence and three reviews can be exemplary. The signal has decoupled from the substance.

This is what a counterfeit web looks like in practice: not the obvious deepfake of a politician, but the slow replacement of the small textual artefacts that hold ordinary commerce together.

The numbers, sourced

The Silicon Canals figure of ninety per cent is, the publication concedes, an aggregated industry estimate rather than a peer-reviewed result. Some consider it an overstatement; others think the proportion of AI-touched content (as distinct from purely AI-generated) is already higher. What is not in dispute is the direction of travel. Studies cited by the publication, including work from the University of Mainz, found participants rated AI-generated and human-generated text as similarly credible, and in some conditions perceived AI prose as clearer and more engaging. The arms race between generators and detectors is being won by the generators.

The detection failure rate is more empirically tractable. Independent benchmarking through 2025 and 2026 has consistently shown no widely deployed detector exceeds roughly eighty-five per cent accuracy across generation models, that even leading detectors miss fifteen to thirty per cent of synthetic content, and that false-positive rates for human prose run three to twelve per cent, with non-native English speakers and technical writers disproportionately misclassified. For a platform processing hundreds of millions of reviews, the maths is grim. Every percentage point of false negative is hundreds of thousands of synthetic items waved through. Every percentage point of false positive is real customers, often the ones with the least linguistic privilege, accused of fakery.

Yelp's report is the cleanest empirical window onto what this looks like at scale. Its trust and safety team filtered nearly half a million suspected AI-generated reviews in 2025, removed over 193,700 reviews flagged by the community (a quarter of which lacked any firsthand experience), and closed roughly 1.3 million accounts for terms-of-service violations, including 889,800 tied to fake airline customer-support scams. The platform reported a 49 per cent rise in accounts linked to “review exchange rings,” a 29 per cent rise in lead-generator business pages, and an 80,000-strong wave of removals tied to viral review brigading. Yelp filed over 1,020 cross-platform reports to Instagram, Facebook, X, LinkedIn, Reddit, TikTok and Craigslist; sixty per cent resulted in third-party action, a 62 per cent improvement on 2024.

The numbers tell a coherent story. The platforms are working harder; the volume is rising faster; the surface beneath everyone's feet is moving.

Why disclosure labels are making it worse

The intuitive policy response to a synthetic-content crisis is to label the synthetic content. The European Commission's Code of Practice on marking and labelling AI-generated content, first drafted on 17 December 2025 and expected to be finalised in May or June 2026 ahead of Article 50 of the EU AI Act coming into force in August, takes precisely this approach. It proposes a common visual marker (a two-letter “AI” icon) alongside machine-readable metadata, allowing users to identify, at a glance, whether content has been generated or substantially manipulated by artificial intelligence.

The trouble, suggested by the Frontiers in Psychology study by Gong, Peng, Cui and Lv, is that disclosure does not behave the way policymakers think. Across two experiments with 760 participants on simulated Bilibili and TikTok-style interfaces, the researchers tested three conditions: clear AI labels (e.g. “content generated by AI”), ambiguous labels (e.g. “suspected AI, please verify”), and no label. The headline finding was uncomfortable. Ambiguous labels significantly increased information avoidance compared to clear labels or no labels, with a Cohen's d effect size of 0.57 versus the no-label condition in the first study, replicated at d = 0.88 in the second. The mediating mechanism was cognitive dissonance: the conflicting signal of “we don't know if this is real” produced enough psychological discomfort that readers disengaged rather than evaluated. They did not weigh the content more carefully. They closed the tab.

The implication is structural. Where a platform cannot distinguish synthetic from authentic with confidence, and so relies on probabilistic, hedging warnings, the labels do not restore trust; they corrode it further. Readers learn quickly that the label is a tax on attention without an information dividend, and stop paying it. The authors propose moving from probabilistic warnings to high-threshold binary classification, leaning on provenance-based authentication rather than detection-based labelling. That maps onto an emerging architecture the standards community has been quietly building for years.

The technical layer: provenance over detection

The Coalition for Content Provenance and Authenticity, known as C2PA, is one of the more interesting institutions to have grown up around the synthetic-content problem. Founded in 2021 as an alliance between Adobe, Arm, Intel, Microsoft and Truepic, hosted under the Linux Foundation's Joint Development Foundation, and now claiming, as of January 2026, more than six thousand member organisations including Google, Meta, OpenAI, Sony, Nikon and Leica, C2PA's premise is that detection is the wrong end of the stick. Instead of asking “is this image AI-generated?” after the fact, the standard asks “what is the cryptographically signed history of this content from the moment of capture or creation?”. Cameras, editing tools and AI generators that implement Content Credentials embed signed metadata describing the origin and edit history of a file. A viewer can inspect the chain of custody.

It is, in principle, the right architecture. Provenance scales where detection cannot, because it does not have to outrun the generators; it sidesteps the race entirely. In practice, however, C2PA has run into uncomfortable empirical realities. As the World Privacy Forum's 2024 technical review noted, very little internet content currently carries C2PA credentials. Worse, the credentials usually do not survive social media sharing, because the major platforms recompress and reformat uploaded images in ways that strip the metadata. An ecosystem-wide rollout still depends on coordinated decisions by platforms, generators, capture-device manufacturers and browser vendors, none of whom have a strong commercial incentive to move first.

The EU AI Act may force the issue. From August 2026, providers of AI systems must ensure machine-readable marking and detectability of AI-generated or manipulated content; deployers must disclose when AI is used to create realistic synthetic media. The draft Code of Practice for transparency leans heavily on the C2PA framework as the de facto reference architecture. Whether the Code, when finalised, manages to push provenance onto the platforms in a form that survives recompression, is, as one Brussels-based standards engineer put it, “the whole game.”

Watermarking, the closely related technique of statistically marking AI-generated outputs at the moment of generation, is making slower progress. OpenAI, Google and Meta have published research on text watermarking, but academic work has consistently shown that watermarks can be removed by light paraphrasing, that they degrade rapidly under translation, and that detection requires access to the model's likelihood functions. None of the major chatbot providers has yet made watermarking the default for free-tier text output. The asymmetry is brutal. A determined adversary needs five seconds of paraphrasing to defeat a watermark; a reviewer who wants to verify it needs cooperation from the model provider and a working detector.

The regulatory layer: three jurisdictions, three theories

Geography matters in this fight, because different jurisdictions have arrived at different theories about what the synthetic-content problem actually is.

In the European Union, the prevailing theory is that the problem is a transparency failure. The EU AI Act, finalised in 2024 with provisions phasing in across 2025 and 2026, treats AI-generated content principally as something that must be labelled and made detectable. Article 50 imposes transparency obligations on both providers (the model makers) and deployers (the platforms and users who run the models in production). Deepfakes must be disclosed unless used for law enforcement or evidently artistic purposes; published AI-generated text on matters of public interest must be flagged; machine-readable provenance must be embedded by providers. Penalties scale, as elsewhere in EU tech regulation, with global revenue. The architecture is the GDPR and Digital Services Act paradigm applied to the substance of content, with the European Commission, working through the AI Office, as the central rule-maker.

In the United Kingdom, the theory is more piecemeal but, in places, more aggressive on consumer-facing harms. The Digital Markets, Competition and Consumers Act 2024 came into force in stages from April 2025, and Schedule 20, the fake-reviews provisions, is the most immediately relevant. The Act bans the commissioning and publishing of fake consumer reviews, defines “fake” expansively to include reviews that purport to be but are not based on a person's genuine experience (which captures AI-generated reviews even where the underlying business does not realise it has commissioned them), and requires platforms to take “reasonable and proportionate” steps to verify authenticity. The Competition and Markets Authority, which acquired direct enforcement powers including the ability to impose fines of up to ten per cent of global turnover, published its CMA208 fake-reviews guidance in 2025 and began enforcement action against several large aggregators that year. Separately, Ofcom, working under the Online Safety Act 2023 and a February 2026 government clarification that closes the loophole around large language model chatbots, can fine platforms the higher of £18 million or ten per cent of global turnover for failure to address illegal content, including AI-generated illegal content carried on user-to-user services.

In the United States, the theory is that the problem is fraud, and the response is consumer-protection enforcement under existing statutes. The FTC's Final Rule on Fake Reviews and Testimonials, finalised in August 2024 and in force from October, prohibits creating, buying or distributing fake or AI-generated reviews, carrying civil penalties of up to $53,088 per violation. On 22 December 2025 the FTC sent warning letters to ten unidentified companies, its first enforcement step. The American architecture is less centralised than the EU model, more reactive, more dependent on case-by-case enforcement, and for now more limited in its leverage over generative AI providers as opposed to the businesses deploying their outputs.

The three regimes share a problem. None was designed for a world in which the cost of generating a plausible review is approaching zero and the cost of verifying it remains, by the testimony of the platforms themselves, stubbornly high.

Brandolini's law, scaled

In 2013, the Italian software developer Alberto Brandolini coined Brandolini's law, the Bullshit Asymmetry Principle: the energy required to refute bullshit is an order of magnitude greater than the energy to produce it. He coined it watching a televised political interview; it has since been applied to anti-vaccination campaigns and cryptocurrency promotion alike. The synthetic-content economy is Brandolini's law expressed in code.

Generating a thousand-word, plausible, contextually appropriate restaurant review with current tooling costs less than half a penny in compute and takes under a second. Verifying that review (by contacting the named diner, cross-referencing the booking system, checking the device fingerprint, examining the IP path, comparing stylometrically against the same author's prior reviews, and adjudicating the result) can take a trust and safety team several minutes of human attention plus several pence of automated compute per item. The asymmetry is not five-to-one or ten-to-one. It is, on the platforms' own internal numbers, several orders of magnitude. Yelp's filtering of half a million suspected AI reviews in 2025 was the visible top of an unknown but likely much larger underwater mass.

The political theorists Bobby Chesney and Danielle Citron, in the California Law Review in 2019, anticipated a related dynamic they called the “liar's dividend.” As the public becomes aware that audio, video and text can be convincingly fabricated, the dividend accrues to liars, who can dismiss authentic embarrassments as deepfakes. Pre-registered experiments with more than fifteen thousand American respondents in the American Political Science Review found the dividend operating reliably against text-based reporting, though largely ineffective against video. The synthetic-content economy generalises this. It is not only liars who benefit, but anyone whose interests are served by the listener being unable to tell the difference, a much larger population.

The wisdom of crowds, considered as a casualty

The aggregator economy was built on a 2004 idea, popularised by James Surowiecki, that under the right conditions large numbers of independent, diverse opinions converge on accurate judgements. The wisdom of crowds is the foundational logic of every star rating you have read. It has always been imperfect: selection bias is significant, manipulation has always been possible. But the basic premise, that a large enough sample of independent human experience reveals something real, has anchored consumer behaviour for two decades.

Synthetic content breaks that premise at the root. The crowd is no longer independent, because one actor can generate a thousand voices. It is no longer diverse, because the underlying language model has its own statistical fingerprint and draws from a narrower distribution than human writers. And it is no longer made of human experience, because the experience never happened. What looks like a wisdom-of-crowds signal is the output of a very small number of decisions amplified to look like consensus.

This is the substantive sense in which the synthetic web is not a degraded version of the old web but a categorically different thing. It does not produce noisier signals. It produces non-signals dressed in the visual grammar of signals. Onora O'Neill, the British philosopher who delivered the BBC Reith Lectures on trust in 2002 and whose work has shaped how regulators and ethicists think about institutional confidence, has long argued that trust requires what she calls “intelligent accountability”: the capacity, in principle, to interrogate the source of a claim, examine the reasoning, and verify the chain. Synthetic content is engineered to resist that capacity. It looks accountable while being structurally unaccountable.

Sissela Bok, the Swedish-American philosopher whose 1978 book Lying and subsequent work at Harvard's Kennedy School made her one of the most cited scholars on the ethics of deception, makes a similar point about the social cost of routine, low-stakes lying. Each individual lie may do limited damage. The cumulative effect of large numbers of lies, normalised, is to deplete the public stock of trust on which all communication depends. The synthetic-content economy is the industrial-scale version of that depletion.

Whose responsibility is it, really

The accountability question is the one regulators, platforms and ethicists are circling. Four candidate answers compete.

The platforms argue they are running moderation at scales no previous information regime has managed, that volume is rising faster than headcount can be added, and that they are investing in detection, cross-platform coordination and rule changes. Yelp's report, in this reading, is not an admission of failure but an account of the work needed to keep the system from collapsing. The platforms are the people inspecting the bridge. They did not build the river.

The model providers argue that they have built guardrails, watermarking research, terms-of-service prohibitions on reputation manipulation and provenance metadata at the point of generation, and that misuse by determined bad actors is at most a partial responsibility, comparable to a typewriter manufacturer's liability for a forged document. They point to the EU AI Act's provider-side obligations as the appropriate institutional response. They made the typewriter. The crime is not theirs.

The regulators argue that statutes have been passed, rules have been finalised, and the task now is to enforce them. The CMA, the FTC, Ofcom and the European Commission have all taken concrete enforcement steps in the past eighteen months. The pace of enforcement is the pace of due process, and due process is slow.

And the users, finally, are told they bear some residual responsibility, on the rationale that any consumer should know better than to trust online reviews unconditionally. This is, by some distance, the weakest of the four arguments. It privatises a cost imposed on people without their consent. The Manchester woman did not cause the synthetic-content economy to exist. She inherited it.

The honest accounting is that all four parties carry responsibility, but unequally. The model providers have prioritised capability over containment, releasing systems whose ability to generate plausible review-style prose vastly outstrips verification infrastructure. The platforms have until recently treated trust and safety as a cost centre. Regulators in all three jurisdictions moved slowly through the 2020s and only since 2024 begun to apply rules with serious teeth. Users have done what users do: use the tools they have been given.

What restoration could look like

A realistic programme for restoring trustworthy informational infrastructure would draw on at least four threads, none of which alone is sufficient.

First, provenance would have to win. Not as a niche feature for professional photographers and journalists but as a baseline expectation, embedded in capture devices, generation tools and platform pipelines, surviving recompression, visible by default. The C2PA standard exists; the EU AI Act may force its adoption in the European market; the open question is whether the United States and the United Kingdom follow, and whether the major social-media platforms can be persuaded or compelled to preserve credentials through their image and video processing pipelines. This is a multi-year project at a minimum.

Second, the reputation economy would have to develop alternatives to pure aggregator-driven review systems. Several promising approaches exist in narrow domains: verified-purchase reviews tied to receipts; closed networks within professional bodies (the Royal Institute of British Architects, the Federation of Master Builders, the CQC's own ratings) that carry the weight of an institution behind each rating; personal-network recommendation systems within messaging platforms, where the trust is borrowed from existing relationships rather than synthesised from strangers. None of these scales as cheaply or as universally as the aggregator model. All are more resistant to synthetic capture, because they tie reputation to something other than easily generated text.

Third, regulation would have to focus less on labelling and more on liability. The Frontiers in Psychology finding suggests disclosure regimes alone are insufficient; the experimental evidence is that ambiguous labels make readers disengage rather than evaluate. A more durable approach, hinted at in the EU's provider-and-deployer architecture and in the FTC's finalised rule, is to assign clear legal liability to the platforms that publish synthetic reviews and to the businesses that benefit from them, regardless of who pressed the generate button. The CMA's powers under the DMCCA, in particular the ability to fine ten per cent of global turnover, are the kind of incentive that can change platform behaviour quickly when applied. The question is whether enforcement will keep pace with generation.

Fourth, social mechanisms would have to be rebuilt at a level beneath the platforms. Local newspapers that survive (and several British regional titles, against the run of play, are growing again under philanthropic and cooperative ownership) carry weight precisely because they are accountable to a defined audience. Community Facebook groups, message-board collectives, neighbourhood WhatsApp networks, and the older mechanism of word of mouth are all forms of trust harder to counterfeit at scale, because they tie reputation to identifiable people in identifiable places. The synthetic-content economy is, in some senses, encouraging a return to these older forms by destroying the credibility of the algorithmic layer above them.

What the inspector said next

The CQC inspector who told the Manchester woman to come and see for herself was disclaiming the digital signal and substituting an institutional one. Implicit in her advice was the older, slower architecture of trust: an inspectorate, accountable to a statutory regulator, whose ratings are produced by named human beings who have walked through the building and spoken to the residents. That signal is expensive to generate (a single CQC inspection takes days of trained-inspector time and is published with a named lead and a methodology); for that reason, it is also very expensive to fake. Authenticity is costly to produce and cheap to verify, because the inspectorate tells you who did the work.

The synthetic-content economy has inverted that asymmetry across most of the consumer web. Restoration, if it is possible, requires inverting it back: making authentic content cheap to verify, by way of cryptographic provenance, and making synthetic content expensive to deploy, by way of liability. Neither half of that programme is technically impossible. Both are politically and commercially difficult, because they impose costs on actors who have, until now, externalised them.

The Manchester woman, in the end, picked her father's care home by visiting four of them in person, talking to staff, talking to residents, reading the most recent CQC report cover to cover, and ignoring the aggregator scores entirely. She found a home with an unfashionable website, a dog that lived on the premises, and a manager who returned phone calls. Her father has been there for six months. She does not know how she would have made the decision if her father had been further away or her time more constrained, and she does not pretend that what worked for her would scale to a country.

That is the awkward truth at the centre of this story. The everyday trust that ordinary consumer decisions depend on was always a public good, sustained by institutions and norms and a basic shared assumption that other people existed. The synthetic-content economy has begun to erode each of those pillars at once. The mechanisms that could restore them, technical, regulatory and social, exist but are partial, contested and slow. The damage in the meantime is being borne by the people least equipped to verify what they are reading: the elderly choosing care, the renters choosing landlords, the patients choosing treatments, the small businesses being review-bombed by competitors with access to a chatbot.

Whether the next decade looks more like a restoration or more like a managed decline depends on whether the institutions still capable of generating trustworthy signals (the regulators, the inspectorates, the standards bodies, the surviving local press, the professional registries) can be given the resources, the legal teeth and the cultural authority to fill the gap left by the failing aggregators. It also depends on whether the platforms and model providers can be made to internalise costs they have, until very recently, been allowed to externalise. There is nothing inevitable about either outcome. The one thing that is certain is that the websites are not what they were, and pretending otherwise is no longer a tenable position.

The counterfeit web is here. The question is what we build alongside it.

References & Sources

1. Brennan, J. (2026). “The AI content flood isn't just an information problem, it's a trust problem.” Silicon Canals, April 2026. https://siliconcanals.com/m-the-ai-content-flood-isnt-just-an-information-problem-its-a-trust-problem/
2. Yelp. (2026). 2025 Trust & Safety Report. Yelp Official Blog, 25 February 2026. https://blog.yelp.com/news/2025-trust-and-safety-report/
3. Yelp Inc. (2026). “Yelp Releases 2025 Trust & Safety Report.” Yelp Investor Relations, 25 February 2026. https://www.yelp-ir.com/news/press-releases/news-release-details/2026/Yelp-Releases-2025-Trust--Safety-Report/default.aspx
4. Gong, Z., Peng, D., Cui, J., and Lv, Z. (2026). “The paradox of AI content labeling: how clarity influences information avoidance via cognitive dissonance on social platforms.” Frontiers in Psychology, Vol. 17, published 10 March 2026. DOI: 10.3389/fpsyg.2026.1751670. https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2026.1751670/abstract
5. Coalition for Content Provenance and Authenticity (C2PA). https://c2pa.org/
6. Content Authenticity Initiative. “How it works.” https://contentauthenticity.org/how-it-works
7. World Privacy Forum. (2024). “Privacy, Identity and Trust in C2PA.” https://worldprivacyforum.org/posts/privacy-identity-and-trust-in-c2pa/
8. European Commission. “Code of Practice on marking and labelling of AI-generated content.” https://digital-strategy.ec.europa.eu/en/policies/code-practice-ai-generated-content
9. EU Artificial Intelligence Act. Article 50: Transparency Obligations. https://artificialintelligenceact.eu/article/50/
10. Jones Day. (2026). “European Commission Publishes Draft Code of Practice on AI Labelling and Transparency.” January 2026. https://www.jonesday.com/en/insights/2026/01/european-commission-publishes-draft-code-of-practice-on-ai-labelling-and-transparency
11. Herbert Smith Freehills Kramer. (2026). “Transparency obligations for AI-generated content under the EU AI Act.” https://www.hsfkramer.com/notes/ip/2026-03/transparency-obligations-for-ai-generated-content-under-the-eu-ai-act-from-principle-to-practice
12. UK Government. Digital Markets, Competition and Consumers Act 2024, Schedule 20 (fake reviews). https://www.legislation.gov.uk/ukpga/2024/13/schedule/20
13. Competition and Markets Authority. (2025). “Fake reviews guidance” (CMA208). https://assets.publishing.service.gov.uk/media/67eeb64fe9c76fa33048c790/CMA208_-_Fake_reviews_guidance.pdf
14. Ofcom. “Ofcom's strategic approach to AI.” https://www.ofcom.org.uk/about-ofcom/annual-reports-and-plans/ofcoms-strategic-approach-to-ai
15. Ofcom. “AI chatbots and online regulation.” https://www.ofcom.org.uk/online-safety/illegal-and-harmful-content/ai-chatbots-and-online-regulation-what-you-need-to-know
16. Federal Trade Commission. (2024). “Final Rule Banning Fake Reviews and Testimonials.” Press release, 14 August 2024. https://www.ftc.gov/news-events/news/press-releases/2024/08/federal-trade-commission-announces-final-rule-banning-fake-reviews-testimonials
17. DLA Piper. (2025). “FTC releases warning letters for fake consumer reviews and AI.” December 2025. https://www.dlapiper.com/en-us/insights/publications/2025/12/ftc-warning-letters-ai-consumer-reviews
18. Chesney, R. and Citron, D. (2019). “Deep Fakes: A Looming Challenge for Privacy, Democracy, and National Security.” California Law Review, 107. https://www.californialawreview.org/print/deep-fakes-a-looming-challenge-for-privacy-democracy-and-national-security/
19. Schiff, K. and Schiff, D. (2024). “The Liar's Dividend: Can Politicians Claim Misinformation to Evade Accountability?” American Political Science Review. https://www.cambridge.org/core/journals/american-political-science-review/article/liars-dividend-can-politicians-claim-misinformation-to-evade-accountability/687FEE54DBD7ED0C96D72B26606AA073
20. Brandolini, A. (2013). The Bullshit Asymmetry Principle. https://en.wikipedia.org/wiki/Brandolini%27s_law
21. O'Neill, O. (2002). A Question of Trust: The BBC Reith Lectures 2002. Cambridge University Press. https://philpapers.org/rec/ONEAQO
22. Bok, S. (1978). Lying: Moral Choice in Public and Private Life. Pantheon Books.
23. Hill Dickinson. (2025). “Digital Markets, Competition and Consumers Act 2024: new consumer law protections now in force.” https://www.hilldickinson.com/our-view/articles/digital-markets-competition-and-consumers-act-2024-new-consumer-law-protections-now-in-force/
24. Burges Salmon. (2026). “Ofcom and the Online Safety Act in 2026.” https://www.burges-salmon.com/articles/102mi1g/ofcom-and-the-online-safety-act-in-2026/
25. UCLA HumTech. “The Imperfection of AI Detection Tools.” https://humtech.ucla.edu/technology/the-imperfection-of-ai-detection-tools/

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Tim Green UK-based Systems Theorist & Independent Technology Writer

Tim explores the intersections of artificial intelligence, decentralised cognition, and posthuman ethics. His work, published at smarterarticles.co.uk, challenges dominant narratives of technological progress while proposing interdisciplinary frameworks for collective intelligence and digital stewardship.

His writing has been featured on Ground News and shared by independent researchers across both academic and technological communities.

ORCID: 0009-0002-0156-9795 Email: tim@smarterarticles.co.uk

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On 9 March 2026, ECRI, the Pennsylvania-based patient safety nonprofit that has been ranking healthcare hazards since the Carter administration, released a document that ought to have detonated through medicine the way the original Institute of Medicine report on medical error did twenty-five years ago. It did not. There were no congressional hearings, no rolling cable news segments, no minute-long agency statements promising action. What there was, instead, was a press release, a few trade-press write-ups, and a particular kind of silence: the silence of an industry that has heard the warning and decided to keep moving anyway.

ECRI's annual Top 10 Patient Safety Concerns is the closest thing American medicine has to an official threat assessment. For 2026, the organisation placed at number one the risk posed by artificial intelligence in clinical diagnosis. Not the chatbots patients talk to in the small hours, not the administrative scribes that write up notes from consultation audio, but the diagnostic systems sitting inside hospital workflows: the algorithms that read mammograms, screen chest X-rays for nodules, flag deteriorating patients on inpatient wards, route radiology priorities, and increasingly draft preliminary impressions that an overworked specialist either confirms or ignores.

The framing was deliberately cautious. ECRI did not call for moratoria. It did not name vendors. It noted, in a tone closer to a risk register than a manifesto, that AI diagnostic systems deployed without rigorous oversight increase the risk of missed, delayed, or incorrect diagnoses; that the data on which models are trained can encode bias; and that clinicians are now operating under the gravitational pull of a phenomenon long studied in aviation and now rapidly being documented in medicine: automation bias, the human tendency to defer to a confident-sounding machine even when the machine is wrong.

What ECRI was really describing, although it did not put it this way, is an accountability vacuum. Clinical AI has arrived in everyday care faster than the legal, regulatory, and institutional architecture needed to govern it. The algorithm is in the room. The clinician is in the room. The hospital, the vendor, and the regulator are all somewhere out of frame. When something goes wrong, and increasingly it does, no one is quite sure where the buck is meant to stop.

How We Got Here Without Noticing

If the ECRI announcement was the warning shot, the State of Clinical AI 2026 report, published two months earlier in January by a multidisciplinary group convened across Stanford and Harvard and their affiliated health systems, was the dispatch from the front line. Led by Peter Brodeur, Ethan Goh, Adam Rodman, and Jonathan H. Chen, the report distilled a year of influential research into a single argument: clinical AI is no longer speculative, no longer the next thing, no longer a topic for a panel discussion at a digital health conference. It is already embedded in care. The question is no longer whether it will arrive but whether the institutions that deploy it can evaluate it honestly once it has.

The report's authors describe a landscape in which AI systems are flagging hospitalised patients at risk of deterioration, assisting radiologists reading mammograms, drafting clinicians' notes, routing patient messages, and increasingly interacting directly with patients through chatbots and digital assistants. They draw a distinction that turns out to be critical: the gap between what AI does well in controlled studies and what it actually does once it is wired into a teaching hospital or a community clinic or a rural primary care practice. The performance figures cited in marketing decks are not lies, exactly; they are simply measurements taken in conditions that no real hospital has ever resembled.

The numbers tell a story of speed. By the early months of 2026, the United States Food and Drug Administration had authorised more than 1,350 AI-enabled medical devices, roughly double the figure from 2022. The European Union's AI Act, which came into force in stages from February 2025, classifies almost every clinical AI system as high-risk and brings its full enforcement regime to bear in August 2026. The United Kingdom's Medicines and Healthcare products Regulatory Agency, the MHRA, has been running its AI Airlock pilot since April 2024 and is expected to publish a new framework for AI in medical devices through the course of 2026. The technology is propagating into clinical workflows on three continents simultaneously, and the institutions tasked with policing it are still drafting the rulebook in public.

That regulatory churn matters because of what sits beneath it. The Stanford-Harvard report's central anxiety is not that clinical AI is bad. It is that nobody yet knows how to tell when it is. Evaluation standards in academic medicine were designed for drugs and devices whose mechanisms could be specified, whose effects could be isolated in trials, and whose failures could be traced. AI diagnostic tools rarely meet any of those conditions. Their behaviour depends on the data they were trained on, the data they encounter in deployment, the workflow they are embedded in, and the disposition of the clinician on the other side of the screen. A model that performs flawlessly at one teaching hospital can quietly degrade at a community hospital ten miles away because the patient population is different, the equipment is older, or the implementation team configured the alert thresholds in a slightly different way.

This is the problem ECRI ranked first. It is not a problem of malice or even of incompetence. It is a problem of opacity at scale.

The Oncology Stress Test

In April 2026, Frontiers in Artificial Intelligence published a peer-reviewed analysis examining the legal and ethical implications of AI failure in oncology. The piece, which built on a body of work going back several years, asked the question that medical lawyers had been chewing on quietly for some time: when an AI tool contributes to a missed or delayed cancer diagnosis, who assumes responsibility?

Oncology is the right stress test. A delayed breast cancer diagnosis can mean the difference between a lumpectomy and a mastectomy, between five years of life and twenty. A missed lung nodule on a chest CT, dismissed as a calcified granuloma by a model that has never seen a tumour quite like this one before, can mean a diagnosis at stage four rather than stage one. The consequences of an oncological miss are, in the technical language of the law, irreversible, and the magnitude of the harm pushes the liability question past the abstract.

The literature converges on a now-familiar list of candidates. The clinician, traditionally the locus of accountability under medical malpractice law, is the first name on the indictment. The hospital, which procured and deployed the system, is the second. The vendor that built and sold it is the third. Each can plausibly be blamed; each can plausibly deflect. The clinician will say the AI told them this finding was benign. The hospital will say it relied on the vendor's regulatory clearance and the clinician's professional judgement. The vendor will point to its end user licence agreement, its disclosed performance data, its assertion that the tool is decision support rather than decision making, and its careful instruction that a clinician must always make the final call.

This is the triple liability puzzle, and it is not new. What is new is the scale at which it now applies. When a single hospital deploys a single proprietary model across thousands of encounters a month, the calculus shifts. A 2024 analysis cited in subsequent legal commentary documented a roughly fourteen percent increase in malpractice claims involving AI tools compared with two years earlier, with the majority stemming from diagnostic AI used in radiology, cardiology, and oncology. Missed cancer diagnoses by machine-learning software have become the central focus of several high-profile cases working their way through the United States court system, although the bulk of these have settled quietly rather than producing the precedent-setting verdicts the field needs.

The peer-reviewed analyses converge on something else, too. The standard of care, that famously slippery legal concept, is moving. In jurisdictions where AI-enabled tools have become demonstrably useful and pervasive, the expectation of what a reasonable physician would do is shifting with them. The clinician who refuses to use a widely adopted AI screening tool may now face liability for not using it. The clinician who uses it and is misled by it may face liability for following it. The doctrine, in other words, is starting to demand that physicians be expert second-guessers of systems whose internal logic they cannot inspect.

The IBM Watson Inheritance

The historical reference point everyone in this debate eventually returns to is IBM Watson for Oncology, the cautionary tale that has become almost ritualistic in clinical AI discussions. Watson, marketed through the 2010s as a cognitive system to help oncologists choose treatment regimens, was eventually shown to be making unsafe and ineffective recommendations in some cases. Internal documentation later suggested that the failures were partly traceable to the way the system was trained: on hypothetical cases curated by a small group of clinicians at one institution rather than on real-world patient data. Watson Health was sold off in 2022. The lesson, repeatedly invoked but inconsistently absorbed, was that an AI system can confidently produce wrong answers because the world it was trained on is not the world it will be deployed in.

Watson is the high-profile cautionary tale. The Epic Systems sepsis prediction model is the more instructive one. Documented in a series of investigations published from 2021 onwards, the Epic Sepsis Model had been deployed across hundreds of American hospitals when an independent external validation by researchers at the University of Michigan, including the work of Karandeep Singh, found that the model missed sixty-seven percent of sepsis cases and that eighty-eight percent of its alerts were false positives. Epic had claimed accuracy of between seventy-six and eighty-three percent. The independent figure was closer to sixty-three.

What made the Epic story matter was less the performance gap than the institutional dynamics it revealed. Hospitals had bought a tool, in some cases under financial incentives that included payments of up to a million dollars to use the algorithm, without seeing an external validation study. Clinicians had spent months responding to alerts that turned out to be wrong most of the time, building up the very automation fatigue that ECRI now warns about. By October 2022, Epic had overhauled the model and was recommending that hospitals retrain it on their own patient data before clinical use, which is itself an admission that the original product was not fit for the purpose for which it had been sold.

No major patient lawsuit emerged from any of this. There was no settlement of consequence. The story passed into the curriculum of clinical informatics conferences as a teaching case rather than a legal one. That, more than anything, is the shape of the accountability problem. The systems propagate, the failures accumulate, the validation lags, and the legal architecture remains, for the moment, stubbornly unable to translate harm into redress.

The Audit Trail That Isn't There

Talk to a medical malpractice plaintiff's lawyer about AI cases, and the conversation eventually arrives at a particular kind of frustration: the audit trail that does not exist. A patient harmed by a delayed cancer diagnosis has historically been able, with effort, to reconstruct what happened. Medical records, while imperfect, exist. Radiologists' impressions are documented. Pathology reports are dated and signed. The clinician's reasoning is, at minimum, partially recoverable.

When AI sits in the chain of decisions, that reconstructibility starts to break down. The output a model produced at a particular moment, on a particular case, with a particular version of the software running, may not be retained. Even when it is, the patient cannot meaningfully access it. Subject access requests under data protection regimes have begun to be tested against this problem, and the results have been uneven. Vendors invoke commercial confidentiality and trade secret protection. Hospitals invoke procurement contracts that limit what they can disclose about the systems they have bought. Regulators have access to internal documentation in principle, but the patient bringing a claim may not.

This is the transparency problem the Stanford-Harvard authors keep returning to. It has two dimensions. The first is technical: many of the models in clinical use, particularly those based on deep neural networks, do not produce outputs whose reasoning can be inspected after the fact in any meaningful sense. There is no chart of inferences. The model produced a probability, and the probability turned into a flag, and the flag turned into a recommendation, and the recommendation either was or was not heeded. The second dimension is institutional. Even where reasoning could in principle be exposed, the legal and commercial architecture of clinical AI deployment is configured to keep it hidden.

The MHRA, in its consultations through 2025 and into 2026, has identified transparency and explainability as core issues. The European Union's AI Act mandates documentation, logging, and human oversight obligations for high-risk systems. California's Assembly Bill 2013, which came into force on 1 January 2026, requires disclosures about training data and use cases for AI systems. None of these instruments yet gives a harmed patient a clean route to find out what an algorithm said about them and why. That is the gap that all the new regulation is, in different ways, trying to close, but the gap is wide and the closure is partial.

What Meaningful Accountability Would Actually Require

Strip away the jargon and the puzzle reduces to a deceptively simple question: what would it look like, in practice, for clinical AI to be accountable in the way that, say, a drug or a surgical device is accountable? The answer has technical, legal, and institutional components, and the slog of the next few years will be in trying to assemble all three at once.

The technical component is the easiest to specify and the hardest to deliver. It would require, at minimum, that any AI system used in a clinical decision retain a tamper-evident log of its outputs at the time of the decision, including the version of the model, the inputs it received, the outputs it produced, and any thresholds or alerts it triggered. This log would have to be retained for a period commensurate with the relevant statute of limitations on medical negligence claims, which in many jurisdictions stretches to a decade or more. It would have to be accessible to the patient and to courts under appropriate process. And it would have to include a meaningful representation of what the model relied on, even when the model is a deep neural network whose internal computations are not human-interpretable. There are technical proposals for this, ranging from saliency maps to counterfactual explanations to surrogate models, but none has yet achieved consensus among clinicians, computer scientists, and regulators.

The legal component is harder. It would require either a new doctrine of AI-specific liability, or the careful adaptation of existing doctrines to the realities of how AI systems behave. The European Union has taken the more aggressive path. The revised Product Liability Directive, working in tandem with the AI Act, classifies software including AI as products and exposes providers to strict liability without the claimant having to prove negligence. When an AI system fails to comply with mandatory safety requirements, it may be presumed defective. The previous eighty-five million euro ceiling on liability for personal injury has been removed. In theory, a patient harmed by a defective AI medical system in the European Union now has a more direct route to compensation than they have in most American jurisdictions, where the tort architecture is still operating on doctrines designed for the bedside, not the back end.

The United States has chosen, so far, to leave most of this to state tort law and FDA premarket review. The FDA's January 2025 draft guidance on AI-enabled device software functions, alongside the agency's adoption from 2 February 2026 of the Quality Management System Regulation aligned with ISO 13485:2016, builds out a more rigorous lifecycle management regime for AI in medical devices. But the agency does not adjudicate harm. It clears products for market. The legal redress for a patient harmed by a cleared device is still routed through the same medical malpractice and product liability channels that have served other medical technologies, with all the difficulty those channels are now exhibiting in cases where the alleged tortfeasor is partly a piece of software.

The institutional component is, in many ways, the most consequential. Hospitals are the connective tissue in this story. They procure the systems. They configure them. They train the staff who use them. They define the policies that govern overrides and exceptions. And they are increasingly the parties best positioned, structurally, to know whether a tool is working. The Stanford-Harvard report's argument is that hospitals must develop the internal infrastructure to evaluate AI systems against their own patient populations, monitor them in deployment, and audit them after the fact. This is not a trivial demand. It implies a category of staffing, a clinical AI governance function, that most institutions have not yet built. Some leading academic medical centres now have such functions. Most community and rural hospitals do not, and many cannot afford to.

Who Has The Power To Demand It

Asking who can demand meaningful accountability in clinical AI is, in the end, an exercise in mapping power. There are six plausible candidates. None of them, in their current configuration, is sufficient on its own.

Regulators have the formal authority but not always the capacity. The FDA has cleared more than 1,350 AI-enabled devices but does not, as a matter of routine practice, conduct postmarket surveillance at the depth the technology requires. The MHRA has explicitly acknowledged that adaptivity, the property of AI systems that change after deployment through retraining or updates, exceeds the regulatory paradigm built for static medical devices. The European Commission's AI Act enforcement architecture is still being assembled, with national competent authorities being designated and notified bodies being built up to handle the volume of high-risk system conformity assessments that August 2026 will trigger. Regulators have power, but it is power exercised at scale across thousands of products, with budgets and staffing that have not grown in proportion to the technology they oversee.

Hospitals have operational authority but face commercial pressure. They are buyers in a market where vendor leverage is significant, where switching costs are high, and where the competing demands of efficiency, finance, and clinician retention all push towards adoption rather than caution. The hospitals best placed to demand transparency from vendors, the major academic medical centres, are also the ones most invested in being seen as cutting edge. ECRI's intervention is, in part, an attempt to give hospital quality and safety officers a vocabulary and a mandate to push back. Whether that mandate will be exercised against multimillion-dollar vendor contracts is another question.

Vendors have the technical capacity. They built the systems. They know, or can know, more about how they behave than anyone else. They have, in most cases, been disinclined to share that knowledge in ways that could be used against them. Some of this is rational commercial behaviour. Some of it is the structural opacity of the technology itself. The vendors, however, are also the actors who will respond fastest to a clear signal from regulators or from major institutional buyers. The market for clinical AI is concentrated enough, and the regulatory pressure global enough, that coordinated demands from a small number of large hospital systems and a small number of regulators could shift vendor behaviour faster than any other intervention. The question is whether such coordination will occur.

Professional bodies have moral authority and limited enforcement power. The American College of Radiology, the Royal College of Radiologists, and equivalent bodies in oncology, pathology, and primary care have begun to issue guidance on the use of AI in clinical practice. These bodies can shape the standard of care, in slow ways. They can influence training, certification, and continuing professional development. They cannot, on their own, force a hospital to retain audit logs or compel a vendor to disclose training data composition. Their power is real but indirect.

Courts are the last-resort accountability mechanism, and they have been notably slow to move. The reason is structural. Most AI-related medical harm cases settle. The discovery process in such cases is expensive and technically difficult. Plaintiffs' lawyers have to work with experts who can credibly testify about model behaviour. Defendants' lawyers have an incentive to settle quickly to avoid creating precedent. The result is that the body of case law that would, in normal medical liability, gradually clarify the standard, is accumulating slowly and out of public view. The Suffolk Journal of Health and Biomedical Law's analysis published in January 2026 noted that this dynamic has been particularly acute in cancer-related AI cases, where the stakes are high enough that defendants are eager to keep matters out of court.

Patients, the population in whose name all of this is being done, currently have the least power of all. They cannot, as a rule, find out which AI systems were used in their care. They cannot, in most cases, opt out. They cannot meaningfully evaluate the performance of the tools applied to them. Patient advocacy organisations have begun to mobilise around AI transparency, and groups working on data protection and informed consent have started to fold AI into their agendas. But the asymmetry of information and the asymmetry of resource between an individual patient and the combined apparatus of vendor, hospital, and regulator is, for the moment, almost total.

The Specific Shape Of The Demand

If this taxonomy of power is right, the question becomes more specific. What is it that any of these actors should actually be demanding?

The first demand, around which something like a consensus is forming across regulatory and academic literature, is mandatory logging. Every clinical AI deployment should be required to retain, in a forensically reliable form, the inputs, outputs, model versions, and decisions associated with each patient encounter. This is technically achievable. It is currently not standard practice. It would, in effect, create the audit trail whose absence is at the heart of the accountability problem.

The second demand is real-world validation. The Stanford-Harvard report's central methodological argument is that controlled trial performance is not a substitute for deployment performance. Hospitals should be required, and increasingly will be required under the EU AI Act and emerging FDA postmarket guidance, to monitor systems in their own environments and to report degradation or drift. This implies a capacity for continuous evaluation that most institutions do not yet have.

The third demand is meaningful transparency to patients. This does not necessarily mean opening the model weights, which most patients would not be able to interpret in any case. It means, at minimum, disclosure that AI was used in the patient's care, what role it played, and where the patient can find further information if they want it. The European AI Act gestures towards this. American practice has been more reticent. The transparency that matters is the transparency available to a patient who suspects something has gone wrong and wants to find out what happened.

The fourth demand is liability clarity. This is the hardest. The European model of strict liability for AI providers under the revised Product Liability Directive is one approach. Another, advocated by some American legal scholars, is enterprise liability, in which the institution that deploys an AI system bears primary responsibility regardless of which actor in the chain caused the harm, with internal apportionment handled through contractual arrangements between hospitals and vendors. A third approach is no-fault compensation schemes, modelled on the vaccine injury compensation framework, that would provide patients with a route to redress without requiring them to navigate the technical complexities of proving that a particular model output caused a particular harm.

The fifth demand is human oversight that is not theatre. The phrase 'human in the loop' has been doing a great deal of work in clinical AI marketing for several years. The reality, as the literature on automation bias documents, is that the human in the loop is often a human under time pressure looking at a confident-sounding suggestion from a system whose internal logic they cannot inspect, with productivity expectations that assume the system is right most of the time. Real human oversight requires workflow design that gives the clinician time, information, and incentive to disagree with the model, and it requires institutional support when they do.

The Politics Of The Vacuum

There is a political dimension to all of this that is harder to discuss in clinical terms but no less consequential. The vacuum in clinical AI accountability did not happen by accident. It is a product of decisions about what to regulate first, how aggressively to regulate it, and whose interests to protect when interests conflict.

The American approach has consistently prioritised speed of innovation. The FDA's evolution from its 2019 discussion paper through the 2021 AI/ML SaMD Action Plan, the 2023 draft guidance on Predetermined Change Control Plans, and the January 2025 draft guidance on AI-enabled device software functions has been a steady accommodation to the realities of AI development, not a containment of them. The European approach has prioritised harmonisation and rights protection, with the AI Act serving as the most visible expression of the bloc's broader posture on technology governance. The United Kingdom has positioned itself as a kind of pragmatic middle, with the MHRA's AI Airlock attempting to enable controlled experimentation while building regulatory capacity.

These are not neutral choices. They reflect different judgements about the proper relationship between technology firms, regulatory institutions, healthcare systems, and patients. The American model accepts a higher level of patient risk in exchange for faster diffusion of potentially beneficial technology. The European model accepts slower diffusion in exchange for more constrained risk and clearer liability. The British model is, depending on how one reads it, either a hedge or an indecision.

What ECRI's number one ranking of AI diagnostic risk for 2026 represents is an assertion, from inside the patient safety community, that the American calibration may be off. That the rate at which clinical AI is being deployed, and the rate at which the institutional architecture to govern it is being built, are not converging fast enough. That the absence of dramatic public failure, so far, is more a function of the kinds of failures these systems produce, which are quiet, dispersed, and individually difficult to attribute, than evidence that no failures are occurring.

What This Looks Like In A Hospital In 2026

A clinician working in a teaching hospital in Boston or Manchester or Munich in April 2026 is operating in an environment where AI is genuinely embedded. The radiologist reading a screening mammogram sees AI-generated annotations overlaid on the images, with the system's confidence scores and BI-RADS suggestions shaping what they look at and in what order. The hospitalist on the wards receives deterioration alerts driven by predictive models that ingest vital signs, lab results, and notes. The oncologist deciding on adjuvant therapy may consult a decision support tool that synthesises guidelines and patient features into a recommendation. The primary care physician in clinic has an AI scribe transcribing the encounter, and possibly drafting the assessment and plan, while they talk.

None of these tools is necessarily bad. Some of them are, on average, helpful. The literature on AI in screening mammography, including the studies analysed in the State of Clinical AI report, suggests that radiologists working with well-designed AI assistance can detect cancers earlier and miss fewer lesions. The literature on deterioration prediction, after the Epic sepsis episode, has matured. AI scribing has documented effects on clinician burnout. The picture is not uniformly grim. The picture is, however, characterised by a chronic mismatch between the scale of deployment and the scale of evaluation.

When something goes wrong inside this environment, the path to accountability is harder than it was a decade ago. The clinician may not have known which model contributed to which decision. The hospital may not have records of the precise system version active at the time. The vendor may have updated the model since. The regulator may have cleared the system on the basis of premarket evidence that does not reflect deployment conditions. The patient, if they suspect harm, may face a discovery process whose costs and complexities exceed the value of even a successful claim.

This is the present. It is not stable. The regulatory pressure building through the EU AI Act, the MHRA's forthcoming framework, the FDA's evolving postmarket guidance, and the gradual accumulation of state-level legislation in the United States all point in the same direction: more documentation, more transparency, more liability clarity. The question is whether the pace of that build-out will keep up with the pace of deployment, and whether the burden of the gap, in the meantime, will continue to fall, as it currently does, on the patients least equipped to bear it.

ECRI's ranking is a warning. The Stanford-Harvard report is a survey. The April 2026 oncology liability analysis is an early diagnosis of a doctrine in flux. None of these documents is, on its own, a remedy. The remedy, if it comes, will be assembled out of the slow work of regulators writing rules, hospitals building governance, vendors disclosing what they would prefer not to disclose, courts producing precedent, professional bodies updating standards, and patients, eventually, demanding the right to know what was decided about their bodies and by whom. The algorithm is in the room. The accountability is not yet. The work, in 2026, is to close that distance before the distance closes the conversation.

References

1. ECRI. 'Top 10 Patient Safety Concerns 2026.' ECRI Thought Leadership Resources, March 2026. https://home.ecri.org/blogs/ecri-thought-leadership-resources/top-10-patient-safety-concerns-2026
2. ECRI. 'AI use in diagnostic care, rural care access, and surge in preventable diseases top annual report of patient safety concerns.' ECRI News, 9 March 2026. https://home.ecri.org/blogs/ecri-news/ai-use-in-diagnostic-care-rural-care-access-and-surge-in-preventable-diseases-top-annual-report-of-patient-safety-concerns
3. Stanford Medicine. 'Clinical AI Has Boomed. A New Stanford-Harvard State of Clinical AI Report Shows What Holds Up in Practice.' Stanford Department of Medicine News, January 2026. https://medicine.stanford.edu/news/stories/2026/01/clinical-ai-has-boomed.html
4. ARISE Network. 'State of Clinical AI Report 2026.' ARISE, January 2026. https://arise-ai.org/report
5. Frontiers in Artificial Intelligence. 'Legal and ethical reflections on the use of artificial intelligence in the diagnosis and treatment of cancer: who assumes responsibility?' April 2026. https://www.frontiersin.org/journals/artificial-intelligence/articles/10.3389/frai.2026.1812408/full
6. New England Journal of Medicine. 'Understanding Liability Risk from Using Health Care Artificial Intelligence Tools.' Mello MM, Guha N. NEJM. https://www.nejm.org/doi/full/10.1056/NEJMhle2308901
7. JCO Oncology Practice. 'Liability Risks of Ambient Clinical Workflows With Artificial Intelligence for Clinicians, Hospitals, and Manufacturers.' ASCO Publications. https://ascopubs.org/doi/10.1200/OP-24-01060
8. United States Food and Drug Administration. 'Artificial Intelligence in Software as a Medical Device.' FDA. https://www.fda.gov/medical-devices/software-medical-device-samd/artificial-intelligence-software-medical-device
9. United States Food and Drug Administration. 'Artificial Intelligence-Enabled Device Software Functions: Lifecycle Management and Marketing Submission Recommendations.' Draft Guidance, January 2025. https://www.fda.gov/media/184856/download
10. Mayo Clinic Proceedings: Digital Health. 'United States Food and Drug Administration Regulation of Clinical Software in the Era of Artificial Intelligence and Machine Learning.' 2025. https://www.mcpdigitalhealth.org/article/S2949-7612(25)00038-0/fulltext
11. European Commission. 'AI Act.' Shaping Europe's Digital Future. https://digital-strategy.ec.europa.eu/en/policies/regulatory-framework-ai
12. EU Artificial Intelligence Act. 'Annex III: High-Risk AI Systems Referred to in Article 6(2).' https://artificialintelligenceact.eu/annex/3/
13. Bird & Bird. 'Liability of Healthcare AI Providers in the EU: How to Navigate Risks in a Shifting Regulatory Ecosystem.' 2025. https://www.twobirds.com/en/insights/2025/liability-of-healthcare-ai-providers-in-the-eu-how-to-navigate-risks-in-a-shifting-regulatory-ecosys
14. UK Government. 'Software and artificial intelligence (AI) as a medical device.' GOV.UK. https://www.gov.uk/government/publications/software-and-artificial-intelligence-ai-as-a-medical-device/software-and-artificial-intelligence-ai-as-a-medical-device
15. UK Government. 'Software and AI as a Medical Device Change Programme roadmap.' GOV.UK. https://www.gov.uk/government/publications/software-and-ai-as-a-medical-device-change-programme/software-and-ai-as-a-medical-device-change-programme-roadmap
16. DLRC. 'AI Airlock: MHRA's Approach to AI in Healthcare.' https://www.dlrcgroup.com/ai-airlock-mhras-approach-to-ai-in-healthcare/
17. Mills & Reeve. 'Regulating AI in healthcare: The UK government wants your input.' Life Sciences Blog, January 2026. https://www.mills-reeve.com/blogs/life-sciences/january-2026/regulating-ai-in-healthcare-the-uk-government-wants-your-input/
18. STAT News. 'Epic's AI algorithms, shielded from scrutiny by a corporate firewall, are delivering inaccurate information on seriously ill patients.' Ross C, Herman B. 26 July 2021. https://www.statnews.com/2021/07/26/epic-hospital-algorithms-sepsis-investigation/
19. STAT News. 'Epic's overhaul of a flawed algorithm shows why AI oversight is a life-or-death issue.' 24 October 2022. https://www.statnews.com/2022/10/24/epic-overhaul-of-a-flawed-algorithm/
20. Michigan Institute for Data Science (MIDAS). 'Dr. Singh and Collaborators Find Private Health Prediction Model Performing Poorly, Despite Widespread Use.' University of Michigan. https://midas.umich.edu/external-validation-of-a-widely-implemented-proprietary-sepsis-prediction-model-in-hospitalized-patients/
21. Radiology (RSNA). 'Automation Bias in Mammography: The Impact of Artificial Intelligence BI-RADS Suggestions on Reader Performance.' https://pubs.rsna.org/doi/full/10.1148/radiol.222176
22. Radiology (RSNA). 'Automation Bias in Breast AI.' https://pubs.rsna.org/doi/full/10.1148/radiol.230770
23. Suffolk Journal of Health and Biomedical Law. 'The New Standard of Care? AI and the Future of Medical Malpractice Law.' 25 January 2026. https://sites.suffolk.edu/jhbl/2026/01/25/the-new-standard-of-care-ai-and-the-future-of-medical-malpractice-law/
24. Medical Economics. 'The new malpractice frontier: Who's liable when AI gets it wrong?' https://www.medicaleconomics.com/view/the-new-malpractice-frontier-who-s-liable-when-ai-gets-it-wrong-
25. Stanford HAI. 'Medicine: The 2026 AI Index Report.' https://hai.stanford.edu/ai-index/2026-ai-index-report/medicine

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Tim Green UK-based Systems Theorist & Independent Technology Writer

Tim explores the intersections of artificial intelligence, decentralised cognition, and posthuman ethics. His work, published at smarterarticles.co.uk, challenges dominant narratives of technological progress while proposing interdisciplinary frameworks for collective intelligence and digital stewardship.

His writing has been featured on Ground News and shared by independent researchers across both academic and technological communities.

ORCID: 0009-0002-0156-9795 Email: tim@smarterarticles.co.uk

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Rebecca Kimble spent more than a decade as an emergency medicine physician, the kind of job described in medical school prospectuses with the word “calling”. She earned between $300,000 and $500,000 yearly. By the early months of 2026, after a long spell of unsuccessful applications back into clinical roles, she was logged into an evaluation interface for an AI laboratory, scoring how well a large language model handled queries about chest pain. She had been technically promoted. She was now an “AI trainer”, paid by the task. There were no benefits, no shifts to hand over. The clients were the foundation model providers whose products were absorbing the work she had spent two decades learning to do.

Kimble's case appeared in a Guardian investigation published in early April 2026 alongside an occupational therapy academic and a software architect now living out of motels, all of them past fifty, all of them refugees from professions where they had built decades of expertise, all of them now annotating data through firms such as Mercor, GlobalLogic, TEKsystems, micro1, Alignerr. The clients listed on the contracts are OpenAI, Google, Meta. The work is unstable. The pay starts at twenty to forty dollars an hour, with specialists occasionally crossing into the low triple digits. Labour economists in the piece called the category a “bridge job” of a cruel sort: high demand now, designed to disappear as the systems being trained on the workers' expertise become competent enough not to need them.

In the same week, Goldman Sachs published a research note that gave the Kimble vignette its longer arc. Written by economists Pierfrancesco Mei and Jessica Rindels, it drew on four decades of individual-level data covering more than twenty thousand workers and asked what happens to a person who loses their job to a wave of technological change. The answer, in the cool register of macroeconomic research, is that they do not, on average, recover. Over the ten years following such a job loss, real earnings for technology-displaced workers grow nearly ten percentage points less than for never-displaced workers, and five percentage points less than for workers displaced by other causes. The phenomenon has a name in the labour economics literature. It is called scarring, and it is not new. What is new is the suspicion, growing now into something close to consensus, that AI will inflict it at a pace and on a population for which no advanced economy has built a meaningful response.

This is a different question from the one that has dominated the AI and jobs debate. That debate has been about aggregates: how many jobs will go, how many will be created, whether the productivity gains will be shared or captured. The question now bearing down is about specific people, and what the rest of us owe them when the machine that took their occupation also took the market for the skills it had taken twenty years to acquire. It is about Kimble, the software architect in the motel, and the millions whose trajectories will not show up in headline unemployment numbers because they will eventually find some kind of job, just not one that adds up to the life they had planned.

The Anatomy Of A Scar

The labour economics of displacement is one of the bleakest sub-fields in the discipline, and it has been bleak for a long time. The foundational empirical work belongs to Steven J. Davis of the University of Chicago and Till von Wachter of Columbia, whose 2011 Brookings paper assembled administrative data on US workers laid off in mass events between 1974 and 2008. Their headline finding has the unsettling quality of a physical law. Workers displaced during a recession lost, in present-value terms, roughly nineteen percent of expected lifetime earnings, a deficit of about $112,100. Workers displaced during expansions lost about half that. Even twenty years after the event, the displaced earned ten to twenty percent less than otherwise comparable workers who had not been displaced. The losses faded only after roughly fifteen years, and even then only partially.

The mechanism, when you decompose it, is not really about unemployment. It is about what economists call occupational downgrading. The displaced worker, eventually, finds a job. The job is in a different industry, often a different occupation, frequently a less skilled one. Whatever firm-specific or industry-specific human capital the worker had built up, the relationships, the tacit knowledge, the accumulated reputation, is largely worthless on the new ladder. The worker starts again, lower down, and never catches up. Davis, von Wachter, and subsequent researchers including Brendan Moore and Judith Scott-Clayton have shown that the firm a worker lands at after displacement matters enormously: workers who can move to a similarly high-paying employer mostly recover, while those who cannot are stuck.

Subsequent NBER work concluded that even prime-aged, well-attached workers suffered persistent losses, that life expectancy fell by roughly one to one and a half years for cohorts displaced in the early 1980s recessions, and that children of displaced fathers earned about nine percent less as adults than peers whose fathers had not been displaced. The scar is not just a wage curve. It is a demographic shadow.

This is the literature that the Goldman Sachs note dropped into. Mei and Rindels's contribution was to ask whether technological displacement specifically, as opposed to displacement from a struggling firm or a contracting industry, produced a distinctive pattern. It does. Workers displaced from technology-disrupted occupations took roughly a month longer to find a new job and suffered real earnings losses more than three percent larger upon re-employment than workers displaced from more stable fields. Their occupational downgrading was sharper because the same forces that took their old job had also degraded the market value of the skills that defined them. A radiologist edged out by an imaging model is competing in a market where the price of radiological expertise has been algorithmically depressed across the board.

Goldman's report singled out one mitigation that worked: workers who participated in a vocational or technical programme within three years of displacement saw roughly two percentage points more cumulative wage growth over the following decade and a ten-percentage-point lower probability of returning to unemployment. The problem, as the same week's Guardian reporting made painfully clear, is that the retraining option that is plausibly on offer to most current AI-displaced professionals is not the one that worked in the 1980s for a machinist becoming a maintenance technician. It is, increasingly, an “AI skills” certificate that the labour market has not yet decided how to value, attached to a person whose previous credential the labour market has just decided not to value at all.

Why This Time May Be Worse

The reflex in any discussion of technological displacement is to invoke the long historical view: weavers and Luddites, telephone operators and steelworkers, eventually superseded by jobs we did not have the imagination to predict. There is something to this. Aggregate employment in advanced economies has, over two centuries, absorbed enormous waves of automation without permanent collapse. The error is in mistaking the long-run aggregate story for the lived experience of the specific cohorts caught between waves.

Three features of the current AI transition make the lived experience plausibly worse than the precedents.

The first is breadth. Earlier waves of automation tended to concentrate on particular sectors, often manual ones. The displaced were geographically clustered, occupationally cohesive, and politically visible enough to demand response, even where the response was inadequate. The post-industrial regions of the US Rust Belt and the British coalfields are not stories of generous adjustment, but they are stories of identifiable communities organised around identifiable losses. AI displacement cuts simultaneously across knowledge work (junior lawyers, paralegals, analysts), creative work (illustrators, copywriters, voice actors), administrative work (claims handlers, customer service), and professional services (consultants, accountants). The displaced are scattered. They will not gather in the same union hall.

The second is speed. The Goldman analysis covered forty years of technological transition, much of which played out across decades. AI capability has compressed similar shifts into months. Anthropic's chief executive Dario Amodei warned in 2025 that AI could eliminate as much as half of entry-level white-collar jobs within five years, a figure widely treated as bombast and widely disputed but consistent enough with what is happening at the firm level that it would be irresponsible to dismiss. Morgan Stanley analysis cited in late 2025 and early 2026 suggested the UK had begun losing more jobs than it created because of AI, performing worse than any other large economy on this measure. Whether or not the most aggressive projections come true, the lived speed of the change has already outrun the period over which retraining schemes are designed to operate. The Goldman finding that retraining helps if it happens within three years is informative; in an AI transition, three years is the gap between two model generations.

The third is the failure mode of the obvious response. The political reflex to AI displacement, in every English-speaking country and across the EU, is some variant of “learn AI”. The UK government's December 2025 announcement of a £965 million plan to push unemployed Gen Z into AI, hospitality, and engineering roles is a faithful illustration. So is the Skills England strategy of distributing AI foundation skills training to ten million workers by 2030, with £136 million for skills bootcamps in the 2025 to 2026 cycle. The premise is that workers who acquire AI skills will be lifted by the same wave that displaced them. The premise is partly true and largely insufficient.

It is partly true because there is a real wage premium attached to demonstrable AI fluency, and workers who use AI tools to multiply their own productivity keep their jobs longer than those who cannot. It is largely insufficient for two reasons. First, the AI skills credential most accessible to a displaced worker (an online course, a bootcamp certificate, a foundation skills badge) is generic, and the wage premium attaches to people who can integrate AI into substantive domain expertise, not to those whose domain expertise has just been devalued. Second, the absorptive capacity of the AI economy for newly minted “AI literate” workers is finite and is saturating faster than retraining pipelines can fill it. The Goldman report's polite phrase for the limit of retraining is “moderately effective”. The Guardian's reporting is the unpolite version: people who did the retraining, or who held the credential before retraining was a slogan, sitting in motels and labelling chest-pain queries.

The Retraining Mirage

Retraining is the policy answer almost every government has chosen and the answer least likely to be sufficient on its own. Brookings Institution analyses since late 2024 have been increasingly explicit about its limits as a stand-alone response, noting that the population most exposed to AI displacement is also the population for whom retraining has historically delivered the smallest returns: mid-career workers with significant prior investment in occupation-specific human capital. The Urban Institute's 2026 report on AI and older workers reaches a similar conclusion. The systems we have are not built for a fifty-five-year-old paralegal whose present skill set was built mostly through doing the job.

Even where retraining works in the technical sense, the credential it produces frequently has no settled labour market value. The proliferation of “AI specialist” microcredentials in 2025 and 2026 has created a thicket of certificates whose meaning is opaque to hiring managers. Some come from elite institutions and carry weight. Some come from for-profit providers whose business model depends on enrolment volumes and whose graduates struggle to demonstrate to employers what the certificate actually attests. The result, documented in the same Guardian reporting and corroborated by labour market data from job-search platforms in the US and UK, is professionals emerging from retraining with a credential that does not function as a substitute for the seniority and domain authority they have lost.

There is a subtler indignity here. The retraining narrative places the moral weight of adjustment on the displaced individual. It assumes the worker has a duty to keep up, a duty to invest in their own continuing employability, a duty to be agile. Many of the displaced workers in the current wave did exactly that. They acquired AI tools, integrated them into their work, used them to make themselves more productive, and were displaced anyway, because the productivity gain accrued mostly to the firm and was eventually used to justify replacing them or their teams with smaller numbers of even more AI-augmented workers, or with the systems themselves. The story that retraining absolves society of further responsibility is one told largely by the parties whose business model benefits from minimising it.

Beyond The Wage Curve

The economics is gloomy. The economics is also not the whole story.

The scarring effect documented by Davis and von Wachter and re-litigated by Goldman shows up in earnings, in unemployment durations, in delayed homeownership, in lower probability of marriage, in shorter life expectancy, in the next generation's earnings. These are measurable outcomes. They sit alongside outcomes that are less measurable but no less real, and that the labour market literature has only recently begun to treat as central rather than incidental. Among them: the loss of occupational identity.

To be a doctor, a lawyer, a teacher, a journalist, a designer, an engineer, is not, for most people who do these things seriously, a means of acquiring income. It is a way of being in the world. It organises time, social relationships, the practice of expertise, the experience of competence. The Boston-area sociologist Allison Pugh has spent fifteen years documenting what she calls “the tumbleweed society”, in which precarious work has corroded the sense of self workers used to derive from steady employment. The current AI displacement wave is not so much extending this trend as detonating it among populations that thought themselves immune. Professional identity, in many of the most-exposed occupations, was the compensating premium that justified years of underpaid training and the assumption of debts. Strip the occupation, and the premium goes too.

There is a parallel cost in retirement security. The post-war social contract in advanced economies relied on a worker spending most of a career in earnings-progressing employment, accruing pension contributions, housing equity, and savings sufficient for a long retirement. A scarring event in the second half of a career, a fifty-something physician dropped to twenty dollars an hour or a marketing director moved into freelance gigs, blows up the pension contribution model and frequently forces drawdown of equity to cover the gap. Existing retirement systems were not built to cushion a decade-long downward shift in earnings late in life. They were built to be supplemented by it. The arithmetic of compounding, working in reverse, is brutal: a contribution missed at fifty-five is several times more consequential to retirement income than the same contribution missed at thirty-five.

The community costs of mass scarring also bear on the discussion. The post-industrial sociology of the US Rust Belt and the UK coalfields, traced in work by Carol Graham at Brookings and the deaths-of-despair literature associated with Anne Case and Angus Deaton, has shown how earnings scarring at scale degrades not just individuals but the social fabric of the places where they live. Falling marriage rates, rising substance abuse, declining civic participation, and the decay of local institutions are downstream of long-term earnings collapse in identifiable communities. The pessimistic projection is that this pattern, formerly geographically contained, will diffuse across the suburbs and commuter belts where knowledge workers are concentrated. Professionals are not immune to despair when their occupations are taken from them.

What The Safety Net Was Built For

The infrastructure that exists to support workers in transition was, almost without exception, designed to handle a different kind of disruption. In the United States, the principal federal programme is Trade Adjustment Assistance, established in 1974 to support workers displaced by import competition. TAA includes a wage insurance component for older workers, paying half the difference between previous and current wages up to a $10,000 two-year cap. Coverage is conditional on demonstrating that displacement was caused by a specifically trade-related shock, a category that has never accommodated technological displacement and is unlikely to start doing so. The TAA data show reasonable outcomes (76.8 percent re-employment, 90.5 percent wage replacement at twelve months) for the small population that qualifies, but the gating is narrow and the overall American unemployment system is famously ungenerous, with state UI typically replacing forty to fifty percent of prior wages for six months or fewer.

The United Kingdom's principal instrument is Universal Credit, supplemented by Jobseeker's Allowance. Universal Credit was designed in the early 2010s to consolidate working-age benefits and to taper support against earnings, and it operates with notional reference rates that are some of the lowest in the OECD. The Institute for Fiscal Studies notes UK unemployment protection is unusually low by international standards, and reforms scheduled for April 2026 introduce a time-limited unemployment insurance benefit somewhat more generous than basic UC. Even after these reforms, the UK system is structurally a poverty-floor system rather than an income-replacement system. It is not designed to soften the multi-year downward slope that scarring describes; it is designed to keep people from destitution while they look for the next job, on the assumption that the next job will be roughly comparable to the last.

Active labour market policy across the OECD, retraining, job-search assistance, employment services, wage subsidies, is more developed in northern Europe than in the Anglophone world. Denmark's flexicurity model, Germany's Kurzarbeit short-time scheme, and Sweden's Trygghetsråden job security councils all reflect a continental bet that proactive transition support beats minimal cash benefits, at resourcing levels several multiples of US or UK equivalents. Even these were designed for a slower, more sectoral pattern of disruption than the present one. The OECD's 2025 Employment Outlook highlights wage insurance and early intervention as priorities, and notes that the policy frontier is shifting towards “career-oriented” support: job mobility, validation of prior learning, active counselling rather than passive cash. The frontier is mostly aspirational. The actual instruments deployed in most countries are still the unemployment insurance schemes built for a manufacturing economy that no longer exists.

The conclusion, which is both obvious and discomforting, is that the safety net in every major advanced economy is calibrated for a transition pace and a displacement pattern that AI is unlikely to produce. It will not catch the people Goldman is describing. It is not designed to.

Proportionality, Or What Would It Actually Take

If the human cost is a multi-year downward shift in life outcomes for millions of individuals, what would a proportionate response look like? The catalogue of plausible answers is not new. What is new is the urgency.

Wage insurance is the most narrowly targeted of the serious proposals, and in some ways the most practical. The mechanism is simple: a worker displaced by a defined cause receives, for a fixed period, a subsidy equal to some fraction of the gap between previous and current wages, with a cap. The TAA wage insurance pilot in the US is one model. A more ambitious version, advocated by Robert Lalonde at the University of Chicago and Lori Kletzer at Pomona among others, would be permanent, uncoupled from trade-specific causation, and set at a replacement rate sufficient to materially flatten the post-displacement income trajectory. Wage insurance is conditional on re-employment, which appeals to centre-right preferences for work incentives, and cushions the scarring slope, which appeals to centre-left preferences for income protection. It does nothing for the displaced worker who cannot find any work.

Portable benefits, the policy bundle developed in the gig economy debate, is the second serious cluster. The premise is that pensions, healthcare entitlements, and accrued leave should attach to the worker rather than the employment relationship, and should be fundable by contributions from any party for whom the worker performs paid work. The displaced professional turned data labeller would continue to accrue pension entitlements from her labelling income; her healthcare coverage would not end with her last salaried role; her capacity to weather the downward slope would be materially improved. Variants of this exist in California, Washington State, and parts of the EU, and the model is spreading slowly under pressure from gig workforce organising. It does not, by itself, address the wage scar. It addresses the cliff edges that surround the scar.

Sectoral transition assistance is the third. Drawing on the European tradition of co-managed transitions, the model dedicates funds and institutional capacity to specific sectors undergoing rapid transformation, providing tailored retraining, job placement, and income bridging for workers leaving the sector. The Trygghetsråden councils in Sweden, jointly governed by employer associations and unions, are the canonical example, with re-employment success rates over eighty percent and substantial wage maintenance for displaced workers. A serious AI-specific application would dedicate sectoral funds to the most-exposed knowledge-work occupations, fund retraining that actually leads somewhere (not generic AI literacy but routes into roles where AI-augmented expertise commands a premium), and provide income bridging for periods longer than the unemployment system contemplates. The cost is non-trivial. The outcomes, where the model has been tried, are markedly better than Anglophone alternatives.

Universal basic income is the fourth, and is the option that most directly engages the question of who pays. The case for UBI in the AI age is that if AI captures a significant fraction of the productivity gain previously realised through human labour, distributing some of that gain unconditionally to the population is the only way to maintain demand and to share the dividend. UK investment minister Jason Stockwood is one of several senior politicians on the centre and centre-left to have endorsed the broad principle in 2026, and the LSE Business Review's 2025 essays on UBI as a new social contract lay out a recognisable framework. The empirical record from limited UBI experiments (Finland, Stockton California, Kenya) is mixed but more positive than detractors allow, particularly on mental health and labour force participation. The political record is harder. UBI is expensive at any meaningful level, and politically vulnerable to the framing that it pays people not to work, a framing that has dogged smaller and more targeted unemployment schemes for decades.

A fifth option, less developed in the policy literature but gaining attention, is a productivity-linked levy on AI-displacing technologies, with proceeds hypothecated to displacement support. Bill Gates's 2017 proposal to tax robots is the rough ancestor; more recent proposals from think tanks including the Roosevelt Institute and academics including Daron Acemoglu would target firms whose AI deployments are demonstrably labour-displacing, using the revenue to fund wage insurance, retraining, and sectoral support. The mechanism is technically tricky: defining a displacing deployment, attributing displacement to specific firms, avoiding incentives to offshore are all hard. The political economy is harder still, because the firms in question include the most powerful corporations in the world, with the most sophisticated tax-policy lobbying capacity in any sector.

Each of these options has live detractors and partial precedents. None of them, individually, would be a sufficient response. Together, in some workable combination, they would begin to look proportionate. None of them is currently being adopted in any advanced economy at the scale that Goldman's findings imply is needed.

The Question Of Who Pays

The question of proportionate response is also a question of moral economy. If millions of workers are pushed onto a decade-long downward earnings trajectory because of decisions made by a few firms deploying a few classes of model, where does the obligation to make them whole sit?

The honest answer, in the existing political economy, is that it sits with the displaced themselves and their families, then with public welfare systems, then with the local communities whose tax bases and social capital absorb the second-order effects. The firms whose products generated the displacement bear, at present, no specific financial obligation tied to it. They bear general corporate tax obligations, of course, with whatever effective rates their tax-planning produces. They bear no levy keyed to displacement, no obligation to fund transitional support for the workers their products replaced, no automatic contribution to retraining schemes, and in most jurisdictions no obligation to disclose the labour market impact of their deployments.

This is, on any reasonable accounting, an enormous externality. The firms that capture the productivity gain do not pay for the wage scarring it causes; the cost is borne by the parties least able to influence the deployment decisions. The standard economic prescription for an externality of this kind is internalisation: a Pigouvian tax that forces the producer to bear the cost their activity imposes on third parties, with the revenue available to compensate those third parties. Applied to AI displacement, that argument is the productivity-linked levy described above. The technical and political difficulties of implementing it are real. The principled case for some version of it is hard to dismiss without abandoning the externalities framework altogether, which orthodox economics is rather attached to.

There is a parallel obligation argument grounded not in externality theory but in distributive justice. The productivity gain from AI is in significant part a return on data and labour that workers themselves contributed, often without meaningful consent, to the training corpora that underlie the systems now displacing them. The Guardian's data labellers are a particularly vivid case: their domain expertise is being directly fed into the systems that will erode the value of that expertise in the broader market. The implicit bargain (your knowledge, in exchange for our model's eventual ability to substitute for you) is one no rational worker would willingly accept. The argument that some share of the productivity gain should flow back to the workers whose accumulated expertise made it possible is, in this framing, not redistribution but restitution.

A third argument operates at the level of state interest. Mass scarring at the scale Goldman describes is not just bad for the affected workers. It is bad for aggregate demand, for public finances, for political stability, and for the legitimacy of liberal-democratic institutions that depend on visible upward mobility for legitimacy. The state has an interest in funding adjustment for reasons independent of any moral claim on AI firms, and a fiscal capacity to do so that is not contingent on extracting revenue from those firms. This is the implicit logic of UK and EU proposals for new unemployment insurance benefits and skills funding, both ultimately taxpayer-funded. The honest objection to this approach is that it socialises losses that were generated by private decisions, and that without a mechanism for capturing some of the corresponding gain, the public balance sheet eventually buckles.

Which of these arguments carries weight is a political question. The state-interest argument has the advantage of being palatable to almost every political constituency and of requiring no novel taxation. It also has the disadvantage of making the public, rather than the AI firms, the residual underwriter of an indefinite transition. The Pigouvian and distributive arguments have the disadvantage of requiring the political defeat of the most powerful corporate lobbies in the world, and the advantage that, if won, they would shift the cost to the parties best able to bear it.

The Person Inside The Statistic

Return to Rebecca Kimble, whose case ran in the Guardian alongside the others, and who is, as far as her interview suggested, more pragmatic than bitter. She is not a metaphor. She is a person who spent twenty years training to do something difficult and useful, who did it for more than a decade, who lost it in a transition not of her making, and who is now adjacent to the systems that took it from her, paid by the task to teach them how to be better at it. The statistical Goldman scar, in her case, is not yet visible, because the data on the current cohort of displaced professionals will not be in for years. On the basis of forty years of prior data, her ten-year earnings trajectory has been bent down by roughly ten percent, and the bend will not straighten.

Multiply Kimble by some number that researchers will eventually settle on. The lowest plausible estimates of AI displacement in advanced economies in the second half of the 2020s run into the millions; the higher estimates run into the tens of millions. Even the lowest estimates imply a population of scarred workers larger than any single cohort affected by any postwar industrial transition. The scale, the speed, and the breadth of the transition, taken together with the inadequacy of the existing safety net and the absence of any meaningful obligation imposed on the firms generating the gains, describe a policy failure waiting to be named.

The Goldman note ended with retraining as its constructive suggestion, the mildest of the available answers and the one most consistent with the existing political settlement. The Guardian's reporting ended with the trainers and motel-dwellers and the accumulating evidence that the settlement is not equal to the moment. Neither paper said what a proportionate response would require, perhaps because both knew that to say it plainly would be to step outside the bounds of what either treats as plausible. It would require, at minimum, the simultaneous deployment of wage insurance, portable benefits, sectoral transition assistance, and a meaningful displacement-linked contribution from the firms whose deployments generated the displacement, all on a scale several multiples beyond what is currently being budgeted in any major advanced economy. It would require, in other words, a different settlement.

Whether one is built before the scar deepens or only after is the question every affected country's political class will, in spite of itself, have to answer. The statistic is being measured. The people inside the statistic have names. The bill is being written, in real time, on the wage curves of millions of careers that will not now arc the way the people living them had assumed.

References & Sources

1. Mei, Pierfrancesco, and Jessica Rindels. “Goldman Sachs Research Note on the Scarring Effects of Technological Displacement.” Goldman Sachs, April 2026. Reported in: Eaton, Kit. “Goldman Sachs Warns That Losing Your Job to AI Can Hurt Your Earnings for a Decade.” Inc., 7 April 2026. https://www.inc.com/kit-eaton/goldman-sachs-warns-that-losing-your-job-to-ai-can-hurt-your-earnings-for-a-decade/91332401
2. Goldman Sachs. “How Will AI Affect the Global Workforce?” Goldman Sachs Insights. https://www.goldmansachs.com/insights/articles/how-will-ai-affect-the-global-workforce
3. TIME. “AI Is Learning to Do the Jobs of Doctors, Lawyers, and Consultants.” https://time.com/7322386/ai-mercor-professional-tasks-data-annotation/
4. Davis, Steven J., and Till von Wachter. “Recessions and the Costs of Job Loss.” Brookings Papers on Economic Activity, Fall 2011. https://www.brookings.edu/wp-content/uploads/2011/09/2011b_bpea_davis.pdf
5. Lachowska, Marta, Alexandre Mas, and Stephen A. Woodbury. “Sources of Displaced Workers' Long-Term Earnings Losses.” NBER Working Paper No. 24217, January 2018. https://www.nber.org/system/files/working_papers/w24217/w24217.pdf
6. Moore, Brendan, and Judith Scott-Clayton. “The Firm's Role in Displaced Workers' Earnings Losses.” Industrial and Labor Relations Review, 2025. https://journals.sagepub.com/doi/10.1177/00197939241310124
7. Brookings Institution. “Unemployment and Earnings Losses: A Look at Long-Term Impacts of the Great Recession on American Workers.” https://www.brookings.edu/articles/unemployment-and-earnings-losses-a-look-at-long-term-impacts-of-the-great-recession-on-american-workers/
8. Brookings Institution. “AI labor displacement and the limits of worker retraining.” https://www.brookings.edu/articles/ai-labor-displacement-and-the-limits-of-worker-retraining/
9. Urban Institute. “AI and Older Workers.” January 2026. https://www.urban.org/sites/default/files/2026-01/AI_and_Older_Workers_0.pdf
10. National Academies of Sciences, Engineering, and Medicine. “Retraining Workers for the Age of AI.” https://www.nationalacademies.org/news/retraining-workers-for-the-age-of-ai
11. Policy Options. “Canada's labour protections aren't ready for the age of AI.” March 2026. https://policyoptions.irpp.org/2026/03/ai-labour-protections/
12. NBER. “Wage Insurance for Displaced Workers.” https://www.nber.org/digest/202408/wage-insurance-displaced-workers
13. CEPR. “Wage insurance for trade-displaced workers: A middle-ground alternative to rising protectionism.” https://cepr.org/voxeu/columns/wage-insurance-trade-displaced-workers-middle-ground-alternative-rising-protectionism
14. Congressional Research Service. “Trade Adjustment Assistance for Workers and the TAA Reauthorization Act of 2015.” https://www.congress.gov/crs-product/R44153
15. Bipartisan Policy Center. “What Happens When Jobs Disappear? A Guide to Displaced Worker Programs in the U.S.” https://bipartisanpolicy.org/explainer/what-happens-when-jobs-disappear-a-guide-to-displaced-worker-programs-in-the-u-s/
16. OECD. “OECD Employment Outlook 2025: Reviving growth in a time of workforce ageing: The role of job mobility.” July 2025. https://www.oecd.org/en/publications/2025/07/oecd-employment-outlook-2025_5345f034/full-report/component-9.html
17. Institute for Fiscal Studies. “Options for unemployment insurance.” https://ifs.org.uk/publications/options-unemployment-insurance
18. Institute for Fiscal Studies. “Universal credit review: challenges and options for reform.” https://ifs.org.uk/publications/universal-credit-review-challenges-and-options-reform
19. UK Government. “Free AI training for all, as government and industry programme expands to provide 10 million workers with key AI skills by 2030.” GOV.UK. https://www.gov.uk/government/news/free-ai-training-for-all-as-government-and-industry-programme-expands-to-provide-10-million-workers-with-key-ai-skills-by-2030
20. Skills England. “AI Skills Boost: Skills England's AI foundation skills for work benchmark supports free AI training for all.” 28 January 2026. https://skillsengland.blog.gov.uk/2026/01/28/ai-skills-boost-skills-englands-ai-foundation-skills-for-work-benchmark-supports-free-ai-training-for-all-by-phil-smith/
21. Fortune. “UK launches $965 million plan to get unemployed Gen Z into AI, hospitality, and engineering.” 9 December 2025. https://fortune.com/2025/12/09/millions-gen-z-unemployed-globally-uk-tossing-965-million-at-problem-get-young-people-ai-hospitality-engineering-jobs/
22. People Management. “Universal basic income needed to support workers displaced by AI, minister says.” https://www.peoplemanagement.co.uk/article/1946845/universal-basic-income-needed-support-workers-displaced-ai-minister-says
23. LSE Business Review. “Universal basic income as a new social contract for the age of AI.” 29 April 2025. https://blogs.lse.ac.uk/businessreview/2025/04/29/universal-basic-income-as-a-new-social-contract-for-the-age-of-ai-1/
24. Pugh, Allison J. “The Tumbleweed Society: Working and Caring in an Age of Insecurity.” Oxford University Press, 2015.
25. Case, Anne, and Angus Deaton. “Deaths of Despair and the Future of Capitalism.” Princeton University Press, 2020.

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Tim Green UK-based Systems Theorist & Independent Technology Writer

Tim explores the intersections of artificial intelligence, decentralised cognition, and posthuman ethics. His work, published at smarterarticles.co.uk, challenges dominant narratives of technological progress while proposing interdisciplinary frameworks for collective intelligence and digital stewardship.

His writing has been featured on Ground News and shared by independent researchers across both academic and technological communities.

ORCID: 0009-0002-0156-9795 Email: tim@smarterarticles.co.uk

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On the second floor of the United Nations Headquarters in New York, in a chamber whose acoustics were engineered for the carefully measured cadence of diplomats, an Mbororo pastoralist from Chad delivered a sentence diplomats are not in the habit of hearing. AI, Hindou Oumarou Ibrahim told the room, becomes harmful when it is imposed without free, prior, and informed consent. The line was lifted from her own report, prepared for the twenty-fifth session of the United Nations Permanent Forum on Indigenous Issues, which opened on 21 April and runs until 1 May. It landed with the dull thump of something said many times before, in many forums, about many extractive industries, and that has not yet changed the rules of the game.

Outside the chamber, on the same continent, the rules of the game were being written by a different hand. On 17 April, an Alberta regulator dismissed the Sturgeon Lake Cree Nation's appeal against a water licence allowing six million cubic metres of annual withdrawal from the Smoky River, water destined to cool a proposed seventy-billion-dollar AI data centre marketed by the celebrity investor Kevin O'Leary as “Wonder Valley”. The nation said it had not been meaningfully consulted; the Aboriginal Consultation Office said no consultation was required. The Smoky watershed is the source of the nation's drinking water and the location of ceremonial and traditional land use sites roughly five kilometres downstream from the proposed diversion point. The trapline, the prayer, and the river all sit at a slightly lower elevation than the cooling tower.

This is the shape of the present, in late April 2026, for indigenous peoples whose territories and knowledge are being absorbed into the infrastructure of artificial intelligence. The forum chamber and the riverbank are the same story told in two languages, one of them legalese, the other hydrology. The arrival of AI on indigenous land is not an isolated event. It is the latest chapter in a five-hundred-year sequence of extractive industries deciding what was on indigenous territory was theirs for the taking. What is new, in 2026, is that the resource being extracted is not a mineral or a forest. It is the cognitive substrate of the communities themselves: their knowledge of plants, of weather, of governance, of language, of what is sacred and what is not.

A Forum, A River, A Crawler

The twenty-fifth session of the UN Permanent Forum on Indigenous Issues, known as UNPFII, took as its overarching theme the protection of indigenous peoples' health, including in the context of conflict. AI was not in the title. It was, however, threaded through the proceedings with an urgency that surprised observers expecting the usual catalogue of mining grievances. Ibrahim, a former chair of the forum, presented a study commissioned to map AI's effects on indigenous communities. Her conclusion, which she repeated in interviews with Mongabay and Grist, was that the technology represents a double-edged sword. AI can be a powerful ally to indigenous stewardship, she said, if it is used on our terms. The conditional was load-bearing.

The terms, in 2026, are not yet ours. Generative AI systems trained on web-scale corpora have already absorbed enormous quantities of indigenous-origin material: oral histories deposited in academic archives, ethnobotanical taxonomies recorded by colonial-era anthropologists, sacred narratives transcribed and uploaded by missionaries or by community members themselves under conditions of trust that did not anticipate machine ingestion. Indigenous languages, often digitised by linguists in preservation projects, now sit inside multilingual models whose outputs are deployed back into indigenous communities as the only available translation infrastructure. Kate Finn, Osage Nation citizen and executive director of the Tallgrass Institute, told the forum the question is no longer whether the extraction has happened. The data is gone. The question is what an enforceable framework of indigenous data sovereignty would look like now, and whether anything like restitution is possible for what has already been taken.

Two arXiv papers published on 23 April, the day after Ibrahim's address, gave the question particular sharpness. The first, “Why are all LLMs Obsessed with Japanese Culture? On the Hidden Cultural and Regional Biases of LLMs”, introduced a benchmark called CROQ, comprising 31,680 open cultural questions across 24 languages, eleven major topics, and 66 subtopics. Its authors documented that frontier language models, when asked to answer a culturally underspecified question, default not to a neutral response but to a small handful of dominant cultural reference points, with Japan emerging as a surprising attractor and Western, English-language assumptions saturating the rest. The bias, they found, is induced predominantly during the post-training and instruction-tuning phase: it is not just a property of the data but a property of the alignment regime the data is filtered through.

The second paper, “Multilinguality at the Edge: Developing Language Models for the Global South” by Lester James V. Miranda, Songbo Hu, Roi Reichart and Anna Korhonen, surveyed 232 papers attempting to build language models for non-English-speaking, hardware-constrained communities. They called the underlying challenge “the last mile”: the place where multilinguality and edge deployment goals align in principle but compete in practice, because the corpora, the compute, and the institutional support do not exist on equivalent terms. Read together, the two papers describe the cognitive infrastructure indigenous peoples will inherit if the current trajectory continues. It is an infrastructure that has already absorbed their knowledge, that does not yet speak their languages well enough to give it back, and whose default settings are not theirs.

Digital Extractivism and Its Older Cousins

The phrase indigenous organisers are using for what is happening to them is data colonialism. Krystal Two Bulls, the Oglala Lakota and Northern Cheyenne executive director of Honor the Earth, used it on Democracy Now! during the forum's opening week and has used it in the organising language of the Stop Data Colonialism coalition, a group of indigenous-led organisations now tracking somewhere between 103 and 160 proposed hyperscale data centres on or adjacent to Native lands in North America. The phrase is not a metaphor. It is a technical claim about the structural similarity between the historical practice of treating indigenous land as a frontier of unowned resources to be incorporated into a colonial economy and the current practice of treating indigenous knowledge as an unowned resource to be incorporated into a commercial AI economy.

The structural similarity is not lost on indigenous organisers, who have lived through the previous iterations. In Oklahoma, the Seminole Nation has unanimously passed a moratorium on hyperscale data centres on its land. In Alberta, the Sturgeon Lake Cree Nation is preparing to take its appeal against the Wonder Valley water licence to the province's superior trial court. In Querétaro, Mexico, residents downstream of new hyperscale facilities are documenting wastewater contamination and groundwater depletion. In Pennsylvania, in Thailand's Chonburi and Rayong provinces, in the U.S. Southwest where mega-projects are siting next to drought-stricken aquifers, the same pattern repeats: facility proposed, water licence applied for, consultation declared adequate by the state, communities not adequately consulted, electricity prices in surrounding areas climbing as much as 267 percent in some Bloomberg analyses, and the gigawatts and the gallons flowing out.

Existing hyperscale data centres have been documented to consume between 300,000 and 2.7 million gallons of water a year per facility, with cooling water and the secondary water embedded in their electricity supply both contributing to a footprint that places enormous load on the watersheds chosen to host them. Those watersheds are not random. They are, very often, the watersheds where land is cheap, water rights are weakly defended, and political resistance is structurally underweighted: in plain language, the watersheds nearest to indigenous, rural, and racialised communities. There is a name for this pattern in the environmental justice literature, and the name is environmental racism. The name has not changed because the pattern has not changed.

What is new, on top of this, is the second extraction. The data centre on the Smoky River is, in addition to a water consumer, a node in a planetary system that absorbs the very knowledge of the communities whose water it is using. This is the recursion that gives data colonialism its peculiar bite. A nation watches a facility built upstream of its trapline, knows the facility's compute is being used to train models that have already ingested the linguistic and ecological knowledge of the trapline, and is then offered the resulting AI assistant as a productivity tool to access government services in the language of the colonising state. The water, the knowledge, and the service are all running in the same direction.

What Was Taken, And How

The taxonomy of what has been taken is concrete. Traditional ecological knowledge, often abbreviated TEK, comprises millennia of accumulated observation about ecosystems: which plants flower when, which fish run with which tides, which soils respond to which fires, which weather patterns precede which migrations. Ethnobotanical knowledge encompasses the medicinal and nutritional properties of thousands of plant species, knowledge that pharmaceutical companies have spent decades attempting to extract through bioprospecting and that AI systems, trained on the resulting academic literature and on community-uploaded forums, can now retrieve in seconds. Oral histories, the substrate of governance and law in many indigenous nations, were transcribed throughout the twentieth century and deposited in archives whose access policies were written before web crawlers existed. Indigenous languages, in projects often initiated with explicit consent of speakers but with no anticipation of generative AI, have been digitised, tokenised, and absorbed into multilingual model corpora.

Some of what has been taken was never meant to leave its community. Sacred or restricted knowledge, governed by indigenous protocols specifying who may speak it, when, and to whom, has often been recorded by outsiders, deposited in archives, and crawled. Under the protocols of the originating nation, this knowledge was never publicly available even if it was technically accessible. The distinction between “publicly available” and “publicly available under the protocols of the originating community” is the distinction the entire commercial AI training pipeline has been built on ignoring. To say that something was on the open web is, in the context of indigenous knowledge, often to say nothing more than that a colonial process of recording and depositing was completed at some earlier date and that no subsequent process of consent has been required.

This matters for restitution because traditional knowledge is, in nearly all indigenous legal traditions, held collectively rather than individually. A song, a story, a botanical recipe, a place name: these have custodians, often specified by lineage or role, but their ownership is the nation's, not the individual's. Western intellectual property regimes, optimised for the individual author and the corporate licensee, are structurally incapable of recognising this form of ownership. The General Data Protection Regulation, often invoked as a model for data rights, is built on individual data subjects exercising individual consent, and provides no purchase for a collective right held by a people. The Convention on Biological Diversity's Nagoya Protocol, adopted in 2010, made the radical move of recognising that traditional knowledge associated with genetic resources triggers benefit-sharing obligations and required parties to obtain prior informed consent of indigenous and local communities for access to such knowledge. It applies, however, narrowly to genetic resources, and operates through state mechanisms that have been uneven in their enforcement.

The instruments closest to a binding standard for the broader case are Articles 11 and 31 of the United Nations Declaration on the Rights of Indigenous Peoples, adopted in 2007. Article 31 states that indigenous peoples have the right to maintain, control, protect and develop their cultural heritage, traditional knowledge and traditional cultural expressions, including their sciences, technologies and cultures, and to maintain, control and develop their intellectual property over such heritage. Article 11 obliges states to provide redress, including restitution, for cultural, intellectual, religious and spiritual property taken without free, prior and informed consent. UNDRIP is a declaration rather than a treaty, and its implementation depends on domestic legislative will, which is precisely the weakness AI training has exploited. The World Intellectual Property Organisation's Intergovernmental Committee on Genetic Resources, Traditional Knowledge and Folklore adopted a treaty in May 2024 requiring patent applicants to disclose the country of origin of genetic resources or associated traditional knowledge underlying their application. By the standards of WIPO, an extraordinary achievement. By the standards of the AI training pipeline, a small object travelling slowly through a window already broken.

The CARE That Is Already Written

A more precise instrument exists, and it has been written by indigenous data scientists rather than by treaty negotiators. The CARE Principles for Indigenous Data Governance, released in September 2019 by the Global Indigenous Data Alliance under the International Indigenous Data Sovereignty Interest Group within the Research Data Alliance, encode a deliberately different premise from the FAIR principles that have dominated open-science discourse since 2016. FAIR asks that data be Findable, Accessible, Interoperable and Reusable. CARE asks that it serve Collective benefit, that those affected have Authority to control it, that those handling it bear Responsibility for the relationships data creates, and that the entire system be subject to indigenous Ethics.

The shift is not cosmetic. FAIR is data-oriented and asks how data can move more freely. CARE is people-oriented and asks for whose benefit, under whose authority, with what accountability, and according to whose ethics. CARE explicitly addresses the asymmetry FAIR's authors did not address: that the move to maximally open data has, in practice, accelerated the extraction of indigenous knowledge by parties with no relationship of obligation to the communities of origin. CARE is intended to be implemented in tandem with FAIR, but its operative force lies in making the openness of FAIR conditional on the consent and benefit structures of CARE.

Apply CARE to AI training data and the shape of an enforceable framework comes into focus. Collective benefit would require that indigenous communities materially benefit from any commercial use of their knowledge, with benefit defined collectively rather than as fees to individual researchers. Authority to control would require communities to be the gatekeepers of inclusion: training corpora would need community-level consent before indigenous-origin material could be incorporated, and ongoing authority to withdraw or restrict that material thereafter. Responsibility would require parties handling the data, model developers, hosting providers, downstream deployers, to take on relational obligations to communities of origin that survive the technical operation of training. Ethics would require that the protocols governing the data be the ethics of the originating community, not the standardised research ethics of the institution doing the training.

This is, on the face of it, an enormous demand. It is also, on a clear reading of UNDRIP Article 31, the existing legal demand of an instrument 144 states have already endorsed. The novelty of CARE is not the principle but the operationalisation. Te Mana Raraunga in Aotearoa New Zealand, the United States Indigenous Data Sovereignty Network, the First Nations Information Governance Centre in Canada, and Maiam nayri Wingara in Australia are already operationalising versions of this framework at the national level. None of the major foundation model providers have signed on to anything resembling it.

Free, Prior, Informed, And Currently Hypothetical

Free, prior and informed consent, abbreviated FPIC, is the operative phrase recurring across UNDRIP, the Nagoya Protocol, and the indigenous data sovereignty movement. The four words are doing a great deal of work. Free means uncoerced by economic dependency or political pressure. Prior means before the act, with enough time for genuine deliberation through the community's own decision-making processes. Informed means with full understanding of what is proposed, including downstream consequences. Consent means refusal must be a real option. In the context of AI training data, the four words are currently hypothetical. No major commercial AI system, in 2026, has obtained anything resembling FPIC for the indigenous-origin material in its training corpus.

A workable framework would need legal recognition of collective indigenous data rights in the jurisdictions hosting the largest AI providers, which means at minimum the United States, the European Union, the United Kingdom, China, and the rest of the OECD. It would need a mandatory training-data provenance disclosure regime, of the sort the EU AI Act gestures towards but does not yet rigorously implement, capable of identifying indigenous-origin material in corpora at the point of training. It would need a mechanism for community-level FPIC operating at the speed and scale of commercial AI development, likely requiring automated tooling built and governed by indigenous data sovereignty bodies rather than by model developers themselves. It would need a right of withdrawal that survives training, which technically requires either model unlearning or retraining without the withdrawn data. It would need a right to negotiate licences on community terms, and crucially the right to refuse altogether. And it would need an enforcement architecture with teeth: regulators willing to fine, courts willing to order takedowns, and procurement regimes that exclude non-compliant systems from public contracts.

None of this is technically impossible. Most of it has been written about in the indigenous data sovereignty literature for at least a decade. The reason it has not been built is not technical. It is that the parties best positioned to build it are also the parties whose business models would be most disrupted by it.

Restitution Is The Hard Part

If the framework above is the prospective question, the harder question is retrospective. What does restitution look like for knowledge already absorbed into Llama, GPT-class models, Gemini, Claude, and the rest? The honest answer is that the menu is short, technically uneven, and politically untested.

The first option is model unlearning, the technical procedure of inducing a trained model to forget specific data without retraining from scratch. The state of the art on unlearning, as of early 2026, is improving rapidly but remains contested in its guarantees. It is one thing to remove an individual user's records from a model. It is quite another to remove the contribution of a community's entire cultural archive, distributed across a vast pretraining corpus, in a way that can be verified to have actually happened. Several recent papers have shown unlearning can leave residual signal recoverable through targeted prompting. Until verifiable unlearning is robust, claims that a model has unlearned indigenous-origin data are claims of intent, not of fact.

The second option is forced retraining, in which providers retrain models without the disputed data, at very large compute cost, and absorb that cost as a condition of operation. This is technically straightforward and politically explosive. It is, however, the option most consistent with the legal logic of UNDRIP Article 11's restitution requirement. If a thing has been taken without consent and cannot be unmade in place, the thing must be unmade and remade.

The third option is compulsory licensing with back-payment to community trusts: existing models continue to operate but providers pay licensing fees, calibrated to scale of use, into trusts controlled by the communities of origin. This is the most politically tractable option and the one most likely to be adopted in any near-term framework. It has obvious shortcomings: it monetises rather than reverses the extraction, places communities in the position of accepting payment for a thing they did not agree to sell, and creates incentives for downstream model providers to argue endlessly about which knowledge counts as indigenous-origin. It also has the advantage of being implementable now.

The fourth option is community ownership stakes in the systems built on top of indigenous knowledge: equity, governance seats, audit rights. This is the most structurally ambitious option and the one most consistent with the indigenous critique that the issue is not the price but the relationship. It would require statutory innovation rather than contractual elaboration, and it would change what an AI company is in a way the industry will resist.

The fifth option is mandatory disclosure of training-data provenance, sufficient to allow communities to identify what has been included and to negotiate from that point. This is the most modest proposal and arguably the precondition for any of the other four.

The sixth option, less concrete but recurring in indigenous testimony, is a reparations fund: a pooled levy on AI providers, administered by indigenous data sovereignty bodies, used to repair the cognitive infrastructure damage the extraction has done. When a multilingual model trained on a community's language is deployed back into the community as the only available digital tool, and when its outputs encode Western assumptions in the community's own grammar, the result is a slow erosion of the community's own ways of meaning. A reparations fund would, on this view, finance indigenous-controlled language technology, indigenous-controlled knowledge management, and indigenous-controlled AI development, on the principle that the appropriate response to colonised cognitive infrastructure is to fund the building of sovereign cognitive infrastructure.

Some of these options are feasible in the near term and others are aspirational. Provenance disclosure is feasible. Compulsory licensing is feasible if political will is generated. Reparations funds are feasible at modest scale. Verified unlearning, forced retraining at scale, and community ownership stakes are aspirational. They define the horizon against which the feasible options should be judged.

The Double Bind

Underneath the legal and technical questions is a deeper one. Even if every framework above were implemented tomorrow, the extraction has happened. The training has occurred. The models exist. The deployment is global. And, increasingly, the AI systems in question are the only available technology in the communities whose knowledge made them possible. Telephony, mapping, translation, education, agricultural advisory, even spiritual chat companions, are migrating to AI substrates whose default settings encode the Western, English-language assumptions documented in the CROQ paper. The community asking an AI assistant about a medicinal plant is asking a system that was, in part, trained on its own ancestors' descriptions of that plant, refracted back through a cultural lens that is not its own.

This is the double bind. The framework that would make the extraction unlawful would not, by itself, undo it. The systems that absorbed indigenous knowledge are now being deployed as essential infrastructure in the territories of the communities they extracted from. To refuse the systems is to refuse the infrastructure. To accept the systems is to accept the colonial overlay. Indigenous AI labs, of which Lars Ailo Bongo's Sámi AI Lab at UiT The Arctic University in Norway is one of a small but growing number, are working on the third option: building indigenous-governed AI on indigenous terms, with indigenous data, for indigenous purposes. Bongo notes the people exist; the funding does not. The Microsoft-Imazon partnership in the Katukina/Kaxinawá Indigenous Reserve in Brazil's Acre state, in which agroforestry agents like Siã Shanenawa use AI tools to monitor deforestation, demonstrates that AI on indigenous terms is possible. It does not, by itself, demonstrate that the broader pipeline can be redirected.

The double bind is not resolvable by clever framework design. It is resolvable, if at all, by a long process of building parallel and sovereign cognitive infrastructure, funded in part by the proceeds of restitution from the extracting industry, in which indigenous communities exercise the right to refuse non-compliant systems and to insist on compliant ones. This is a generational project. It requires the framework be put in place now, in 2026, so that the work of building can begin under the protection of law rather than against it.

What Would Be Required

Any honest editorial position on this matter has to begin with a refusal of the comforting framing that what is needed is more research, more dialogue, more fora. The research has been done. The dialogue has been held. The fora are filled with documentation. What is missing is an enforcement architecture and the political will to install it.

Any workable framework has, at minimum, the following shape. It begins with the legal recognition, in the major AI-hosting jurisdictions, of collective indigenous data rights as a category distinct from individual data subject rights. This is statutory work. It requires legislatures, not voluntary corporate codes. The EU AI Act and the GDPR can be the basis for this in Europe, but they require explicit amendment to recognise collective subjects. In the United States, tribal sovereignty already provides a legal foundation that has been systematically underused.

It requires a mandatory provenance disclosure regime granular enough that indigenous-origin material can be identified and that communities can exercise meaningful FPIC. It requires that FPIC be obtained before training, not after, and at the level of the originating community rather than from an individual or from a state acting on behalf of the community. It requires the right of withdrawal, with a workable technical pathway for fulfilment, whether through unlearning, retraining, or operational restriction. It requires that the CARE Principles be elevated from a research community framework to a regulatory baseline. The Global Indigenous Data Alliance has done the operationalisation work; the remaining task is binding adoption.

It requires a restitution architecture for the knowledge already taken. The most realistic near-term shape is a compulsory licensing regime, with payments flowing into community-controlled trusts, combined with provenance disclosure that allows communities to identify what has been used. The more ambitious shape, which the editorial position of this article supports, is a reparations levy whose proceeds fund indigenous-governed AI infrastructure, on the principle that the appropriate response to colonised cognitive substrate is sovereign cognitive substrate.

And it requires, at the level of physical infrastructure, that data centre siting be subject to the same FPIC standard as the data inside the centres. The Sturgeon Lake Cree Nation's appeal of the Wonder Valley water licence is the test case in the Canadian context. The Seminole Nation's hyperscale moratorium is the test case in the American one. The result of these cases will indicate whether courts and regulators are prepared to apply the same logic to data centres that the Nagoya Protocol applied to bioprospecting.

The honest closing observation is that none of this will happen because the AI industry chooses it. It will happen, if it happens, because indigenous nations, environmental justice coalitions, and the regulators willing to be moved by them, force it to happen. Krystal Two Bulls and Honor the Earth are organising for that. Hindou Oumarou Ibrahim is presenting reports for it at the UN. Kate Finn and the Tallgrass Institute are working with investors who have the leverage to demand it. The Global Indigenous Data Alliance has written the operational template. The CROQ benchmark has documented the cultural bias the framework would have to correct. The Multilinguality at the Edge survey has mapped the technical landscape on which sovereign indigenous AI will have to be built. The materials are present. What is needed is the decision to use them, and the political pressure to make that decision unavoidable.

The knowledge that sustains a community's relationship with its land, its language, and its identity was never the AI industry's to take. It has been taken. The question is no longer whether that was wrong. The question is whether the framework that would prevent it from happening again, and the restitution that would begin to repair the damage already done, will be built in time to matter. The river above the data centre is still flowing. The community downstream is still there. The forum chamber is still in session. The clock is louder than any of them.

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References & Sources

1. United Nations Department of Economic and Social Affairs. “United Nations Permanent Forum on Indigenous Issues, 25th Session.” https://social.desa.un.org/issues/indigenous-peoples/unpfii/25th-session
2. Mongabay. “AI is a double-edged sword for Indigenous stewardship, say U.N. experts.” April 2026. https://news.mongabay.com/2026/04/ai-is-a-double-edged-sword-for-indigenous-stewardship-say-u-n-experts/
3. Mongabay. “War, climate change, and AI on the agenda at this year's U.N. Indigenous forum.” April 2026. https://news.mongabay.com/2026/04/war-climate-change-and-ai-on-the-agenda-at-this-years-u-n-indigenous-forum/
4. Grist. “AI is a double-edged sword for Indigenous land protection, UN experts warn.” April 2026. https://grist.org/indigenous/ai-is-a-double-edged-sword-for-indigenous-land-protection-un-experts-warn/
5. Grist. “Indigenous land defenders are being killed, and AI is scraping their knowledge.” April 2026. https://grist.org/indigenous/indigenous-land-defenders-are-being-killed-ai-is-scraping-their-knowledge/
6. Grist. “For Indigenous communities, AI brings peril and promise.” https://grist.org/indigenous/indigenous-peoples-examine-impact-of-ai-on-communities/
7. Democracy Now! “Data Colonialism: Native Communities Fight AI Data Centers on Indigenous Land.” 22 April 2026. https://www.democracynow.org/2026/4/22/krystal_twobulls_indigenous_lands_data_centers
8. Mongabay. “AI data center revolution sucks up world's energy, water, materials.” November 2025. https://news.mongabay.com/2025/11/ai-data-center-revolution-sucks-up-worlds-energy-water-materials/
9. Mongabay. “Thai data center boom sparks fears of water shortage, air pollution.” March 2026. https://news.mongabay.com/2026/03/thai-data-center-boom-sparks-fears-of-water-shortage-air-pollution/
10. Mongabay. “The Cloud vs. drought: Water hog data centers threaten Latin America, critics say.” November 2023. https://news.mongabay.com/2023/11/the-cloud-vs-drought-water-hog-data-centers-threaten-latin-america-critics-say/
11. Miranda, Lester James V., Songbo Hu, Roi Reichart, and Anna Korhonen. “Multilinguality at the Edge: Developing Language Models for the Global South.” arXiv preprint arXiv:2604.21637, 23 April 2026. https://arxiv.org/abs/2604.21637
12. “Why are all LLMs Obsessed with Japanese Culture? On the Hidden Cultural and Regional Biases of LLMs.” arXiv preprint, 23 April 2026. https://arxiv.org/html/2604.21751
13. Global Indigenous Data Alliance. “CARE Principles for Indigenous Data Governance.” September 2019. https://www.gida-global.org/care
14. Carroll, Stephanie Russo, et al. “The CARE Principles for Indigenous Data Governance.” Data Science Journal, vol. 19, no. 43, 2020. https://datascience.codata.org/articles/10.5334/dsj-2020-043
15. Carroll, Stephanie Russo, et al. “Operationalizing the CARE and FAIR Principles for Indigenous data futures.” Scientific Data, 2021. https://www.nature.com/articles/s41597-021-00892-0
16. United Nations. “United Nations Declaration on the Rights of Indigenous Peoples.” 2007. https://www.un.org/development/desa/indigenouspeoples/wp-content/uploads/sites/19/2018/11/UNDRIP_E_web.pdf
17. Convention on Biological Diversity. “Nagoya Protocol on Access and Benefit-sharing.” Entered into force 12 October 2014. https://www.cbd.int/abs/
18. World Intellectual Property Organisation. “Intergovernmental Committee on Intellectual Property and Genetic Resources, Traditional Knowledge and Folklore.” https://www.wipo.int/tk/en/igc/
19. Canada's National Observer. “'Absolute failure': First Nation slams Alberta and Kevin O'Leary's data centre moves.” 24 April 2026. https://www.nationalobserver.com/2026/04/24/news/wonder-valley-kevin-o'leary-alberta-first-nation
20. CBC News. “Alberta First Nation voices 'grave concern' over Kevin O'Leary's proposed $70B AI data centre.” https://www.cbc.ca/news/canada/edmonton/alberta-first-nation-voices-grave-concern-over-kevin-o-leary-s-proposed-70b-ai-data-centre-1.7431550
21. ICT News. “In Indian Country, data centers come with a familiar threat of colonialism. These organizers are fighting back.” https://ictnews.org/news/in-indian-country-data-centers-come-with-a-familiar-threat-of-colonialism-these-organizers-are-fighting-back/
22. Honor the Earth. “Stop Data Colonialism Campaign.” https://www.honorearth.org/stopdatacolonialism
23. High Country News. “War, climate change and AI are at stake at the 2026 UN Indigenous forum.” April 2026. https://www.hcn.org/articles/war-climate-change-and-ai-are-at-stake-at-the-2026-un-indigenous-forum/
24. Futurism. “Tech Companies Are Using Insidious Tactics to Build Data Centers on Indigenous Lands, Activists Say.” https://futurism.com/artificial-intelligence/data-centers-tribal-communities

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Tim Green UK-based Systems Theorist & Independent Technology Writer

Tim explores the intersections of artificial intelligence, decentralised cognition, and posthuman ethics. His work, published at smarterarticles.co.uk, challenges dominant narratives of technological progress while proposing interdisciplinary frameworks for collective intelligence and digital stewardship.

His writing has been featured on Ground News and shared by independent researchers across both academic and technological communities.

ORCID: 0009-0002-0156-9795 Email: tim@smarterarticles.co.uk

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The room is unremarkable. A clean desk, a laptop, a printed sheet of arithmetic and short reading passages, and a pair of EEG electrodes resting against the scalp like patient fingers. The participant has just finished ten minutes of working through problems with the help of an AI assistant. The screen is closed. The chatbot is gone. A research assistant slides a fresh page across the desk and asks, politely, for the subject to answer the next set of questions alone.

The subject reads. The subject thinks. The subject, by every behavioural and neural measure the researchers can capture, performs measurably worse than a control participant who never touched the assistant. The effect is not subtle. It is there in the response latencies, in the error rates, in the EEG traces that show a dampened pattern of frontoparietal engagement which, ten minutes earlier, was healthy and robust.

That, in essence, is the claim of a multi-institution study widely reported across science media in April 2026, attributed to researchers from UCLA, MIT, Oxford and Carnegie Mellon, which proposes the first causal evidence that brief AI use is sufficient to produce immediate, measurable cognitive impairment in the unaided performance of equivalent tasks. The reporting has been breathless and the framing predictably apocalyptic, but the scientific stakes, if the finding survives replication, are genuinely large. Earlier work in this area had described a slow drift, a kind of boiling-frog dependency in which years of cognitive offloading might thin out a person's capacity to think for themselves. The newer claim is something different and arguably more disturbing: that the cost shows up in minutes, not years.

The distinction is not academic. If the harm is gradual, you can argue, with some plausibility, that informed adults using AI in the privacy of their own choices are merely making a long-term trade-off they are entitled to make. If the harm is acute, then the deployment of AI assistants in classrooms, clinical consulting rooms, courtrooms, contact centres and welfare offices, often without disclosure and almost always without anything resembling informed consent, looks rather different. It looks like a very large and largely unmonitored field experiment.

What does the evidence actually show? What can be defended, and what cannot? And once we are honest about both, who has the responsibility to act?

The Boiling Frog and Its Discontents

For the past three years, the dominant frame for thinking about AI and cognition has been the boiling frog, the apocryphal creature that fails to leap from a gradually heating pot. The framing made sense because the foundational evidence in cognitive science was itself longitudinal and slow.

Eleanor Maguire's work at University College London on the hippocampi of London taxi drivers, beginning in 2000, established that the brain regions used to navigate a complex city physically thicken with use. Subsequent imaging work, including a 2017 study in Nature Communications by Hugo Spiers and colleagues, suggested that turn-by-turn satnav use suppressed activity in the same hippocampal circuits. Capacity follows demand: ask the brain to navigate, and it grows the apparatus for navigation; ask it to follow instructions from a phone, and the apparatus quietens.

In 2011, Betsy Sparrow, then at Columbia, with Jenny Liu and the late Daniel Wegner of Harvard, published a paper in Science showing that participants who expected to look information up later remembered the information itself less well, but remembered where to find it. A 2024 meta-analysis in the journal Memory found the Google effect real but more modest than early coverage suggested.

Together, this literature painted a picture of slow, accumulative externalisation. Bit by bit, certain cognitive functions migrated from the wet hardware in the skull to the dry hardware in the pocket. The implicit settlement was that the costs were chronic and perhaps reversible if you put the phone down.

Generative AI complicated this picture, but for the first eighteen months of the consumer chatbot era the public discussion still defaulted to the chronic frame. Even Michael Gerlich's much-cited 2025 paper in Societies, which surveyed 666 participants and reported a strong negative correlation between AI tool use and critical thinking scores, was best read as a snapshot of ongoing erosion rather than a claim about acute injury.

Acute injury is what the newer reporting is now claiming. And acute injury, scientifically and ethically, is a different beast.

What An Acute Effect Would Have to Mean

To understand why the reported April 2026 finding has provoked the reaction it has, it is worth being precise about what an acute cognitive effect would, and would not, be. An acute effect appears rapidly after exposure and is measurable on a short timescale. A chronic exposure might gradually wear down an organ over decades; an acute exposure produces a measurable change within minutes or hours.

In the cognitive context, the equivalent claim is that ten minutes of AI-assisted maths or reading leaves a measurable footprint on the brain's ability to perform similar tasks unaided immediately after. The footprint, if it exists, is not memory loss in any everyday sense. It is more like a transient state shift, a cognitive tone that has slackened.

This is not biologically implausible. Cognitive psychology has long documented carry-over effects between tasks. Mental set, the tendency to apply a problem-solving strategy beyond its useful range, is a textbook example. So is the well-replicated finding that performing a task in a state of high external scaffolding can degrade subsequent independent performance, a phenomenon educators have long known as the assistance dilemma.

What the reported study would add is an EEG-level signal, that the brain is not merely behaving as if it has just been scaffolded but is in some quantifiable sense still in the scaffolded state, with reduced engagement in the networks that would ordinarily be doing the work. If that signal replicates, the implication is that AI use is not merely a labour-saving device whose benefits and costs balance out neatly. It is a state-altering one.

This is where the strongest existing evidence in the literature, the MIT Media Lab paper Your Brain on ChatGPT: Accumulation of Cognitive Debt when Using an AI Assistant for Essay Writing Task, becomes essential context. Authored by Nataliya Kosmyna and seven co-authors and posted to arXiv in 2025 as preprint 2506.08872, the paper studied 54 participants in the Boston area aged 18 to 39, who wrote SAT-style essays under one of three conditions: with a large language model, with a search engine, or with no tools at all. EEG recordings during writing showed that brain-only participants exhibited the strongest, most distributed network engagement; search engine users showed moderate engagement; and LLM users displayed the weakest connectivity. Eighty-three per cent of LLM users were unable to quote from the essays they had just produced. In a fourth session, when LLM users were reassigned to brain-only writing, they continued to show weaker neural connectivity than the consistent brain-only group. The MIT authors called this carry-over cognitive debt.

The MIT preprint was not peer reviewed when it was posted, the sample was modest, and the authors themselves cautioned against the most sensational interpretations. But the basic shape of its finding, that there is a residual neural signature after the AI is taken away, is precisely the shape of the claim that the April 2026 reporting is now amplifying. The newer study, on the description circulating in the science press, extends the logic to elementary cognitive tasks rather than essay writing, and to far shorter exposures.

It is too early to know whether the April 2026 work will hold up under peer review and replication. It is not too early to ask what the world should do if it does.

Cognitive Offloading, Without Romance

The mechanism most often cited for both the chronic and acute findings is cognitive offloading: the use of external tools to reduce the demands on internal cognition. The concept predates the AI debate by decades. Writing things down is cognitive offloading. So is asking a colleague. Offloading reduces working-memory load and frees attention. Under certain conditions, it also reduces depth of processing, weakens encoding into long-term memory, and degrades the capacity to do the offloaded task without the tool.

What seems to be different about generative AI is the scope, the seamlessness and the ambient nature of the offload. A calculator does arithmetic. A search engine fetches documents. A large language model writes the paragraph, generates the answer, structures the argument and presents the result in finished form. The cognitive task it performs is not retrieval but synthesis, the very thing that, in classical accounts, is supposed to constitute the active work of thinking.

The Microsoft Research and Carnegie Mellon paper presented at CHI 2025 in Yokohama, authored by Hao-Ping Hank Lee and colleagues and based on a survey of 319 knowledge workers analysing 936 real-world AI-assisted tasks, gives this dynamic empirical shape in the workplace. The paper found that higher confidence in AI was associated with less critical thinking, while higher self-confidence in one's own abilities was associated with more critical thinking. The authors described a shift in the nature of cognitive work itself, from information gathering toward verification, from problem-solving toward integration of AI output, from doing toward supervising. They warned of what they called cognitive atrophy.

The proposed mechanism for an acute effect, then, is not mysterious. During AI-assisted work, the cognitive networks responsible for evaluating and integrating outputs remain active. The networks responsible for original generation, planning and synthesis quieten down. When the tool is taken away, the still-quietened networks do not instantly come back online. There is a lag. The lag is what the EEG picks up. The lag is what shows up in error rates and response latencies on the unaided task that follows.

This is, importantly, not a permanent change. Nothing in the existing literature suggests that ten minutes of AI use causes structural damage to the brain. The relevant concern is not about lasting injury but about state, about the cognitive tone in which the next task is begun, and how quickly that tone recovers when the scaffolding is withdrawn.

Reversibility, And What We Do Not Yet Know

Here the honest answer is that the science is at the very beginning of being able to say anything precise. The MIT preprint hints at carry-over within and across sessions, but its design does not isolate the time course of recovery. The reported April 2026 work claims acute impairment immediately after use; it does not, on the publicly available descriptions, characterise the recovery curve in detail. We have evidence of a measurable effect on the order of minutes after AI use, and we do not yet have systematic evidence about whether that effect dissipates within an hour, a day or a longer period, nor about whether repeated daily exposures produce cumulative residue.

The plausible space of outcomes is not fanciful. If the effect resolves quickly and completely after each exposure, it is roughly analogous to the post-meeting fog that anyone who has spent two hours in a video call recognises, an irritation that fades. If it resolves slowly, or if repeated exposure produces cumulative dampening, the deployment-context implications become substantial. A nurse consulting an AI scribe before a complex assessment, a teacher grading with an AI marker before lesson planning, a junior solicitor moving from AI-drafted briefs to in-court argument: all are scenarios in which acute carry-over, even if reversible, has the potential to land on the high-stakes unaided task that follows.

The claim that needs neither hyping nor dismissing is the modest one. There is evidence, from multiple research groups and instruments, that recent AI use leaves a footprint on subsequent unaided cognition. The size of that footprint, its time course, and its dependence on the type of task, the type of AI and the individual user, are all open questions.

Where The Footprint Lands

The deployment contexts in which acute carry-over would matter most are, helpfully, the same contexts in which AI is being most aggressively deployed. They are not the recreational ones. Nobody is particularly worried about the cognitive aftermath of asking a chatbot to write a wedding speech. The relevant contexts are workplaces where consequential decisions are made under time pressure, classrooms where developing minds are still acquiring the very skills that AI is offloading, healthcare settings where lapses cost lives, and public services where outcomes determine whether citizens are housed, fed, treated or detained.

Take healthcare. In the United Kingdom, AI scribes and clinical-decision-support assistants have proliferated in primary care since 2024, with the Department of Health and Social Care actively encouraging the use of approved tools to reduce administrative burden on general practitioners. The case for these tools is strong; clinician burnout is a public-health emergency in its own right, and time spent transcribing is time not spent with patients. But the consultation that follows the AI-assisted note is not a low-stakes task. It is the next patient. If the cognitive tone with which the clinician enters that next consultation is even slightly slackened by the immediately preceding offload, the relevant question is not whether the tool, on average, saves time. It is whether the unmonitored carry-over is being detected, accounted for, or even acknowledged.

In classrooms, the acute frame inverts the existing debate. The debate so far has largely been about whether students who use AI to do their homework will eventually fail to learn how to write or reason. The acute frame asks a different question: what does it mean to ask a student to use an AI in the first half of a lesson and then to demonstrate understanding in the second? If the cognitive state in which that demonstration happens is materially different from the state of a student who never used the tool, then the assessment is not measuring what it purports to measure. The Department for Education's June 2025 guidance on AI in schools acknowledged that students still needed a strong foundation in reading, writing and critical thinking to use AI effectively. The acute literature, if it stabilises, suggests the guidance does not go nearly far enough. It is not enough to know how to use the tool. The question is what happens when you put it down.

In workplaces more generally, the carry-over question intersects with the dynamic identified by Lee and colleagues at CHI 2025: workers shifting from generation to verification, from problem-solving to integration. If the verification mode itself depends on a cognitive state that is, in the moment, dampened by the just-preceding AI exposure, then verification is precisely the function being undermined. The dynamic is recursive.

In public services, the stakes are starkest. Algorithmic systems already mediate decisions about welfare entitlements, child-protection assessments, criminal-justice risk scoring and immigration triage in many jurisdictions. The case-workers operating those systems are increasingly being given AI-assisted summarisation, drafting and recommendation tools. The decisions they then make about real human lives are made, in some cases minutes after closing the assistant. Whether the cognitive tone in which those decisions are made is materially different from the tone of an unaided counterpart is not a niche concern. In the deployment contexts that matter most, it is the central one.

The Consent That Was Never Asked For

The ethical literature on technology adoption has historically operated on a strong presumption: that adults, when offered new tools, are entitled to choose to use them, and that the costs of choosing are theirs to bear. This presumption rests on a thicket of assumptions which the acute-impairment frame, if it survives, calls into question.

The first assumption is that the user is the one bearing the cost. In the workplace, that is rarely true. A nurse using an AI scribe is not the principal bearer of the risk if her cognitive tone in the next consultation is dampened. The patient is. A teacher using an AI marker is not the principal bearer if his unaided judgement in the next lesson is reduced. The student is. The deployment of AI in service contexts shifts the costs onto people who were not party to the decision and who, in many cases, do not even know the tool was used.

The second assumption is that the user has been informed. This is, in practice, almost never the case. The patient who interacts with a clinician using an AI scribe is not, in the United Kingdom or in most other jurisdictions, given any disclosure that the scribe was used, much less that recent research has suggested an acute carry-over effect on subsequent unaided cognition. The student whose teacher has just spent half an hour grading essays with an AI marker is not informed. The benefits claimant whose case-worker's notes were drafted by a generative system is not informed. There is, in most settings, no equivalent of the medical-imaging consent form, no equivalent of the data-protection notice, no equivalent of any of the layered consent infrastructures that exist for less consequential interventions in the same lives.

The third assumption is that the cognitive risk is well characterised. It is not. The literature on acute carry-over is, at the time of this writing in April 2026, weeks old in its strongest formulations and months old in its broader contours. Honest informed consent at present would have to read something like: research suggests, but has not yet established, that recent AI use may produce a transient reduction in unaided cognitive performance, the magnitude and duration of which are not yet well understood. That is not a notice that any organisation in any sector is currently required to provide.

The result is a deployment landscape in which a class of cognitive risk has been quietly normalised across millions of high-stakes interactions, on the strength of an implicit assumption that the science was either not real or not relevant, and without any of the consent infrastructure that would be required to make the deployment ethically defensible if the science turns out to be both.

Who Is Currently Responsible, And Who Currently Is Not

The question of who has the responsibility to act on the emerging evidence has, at present, no clean answer. There is a thicket of actors with partial responsibilities, and a great deal of empty space between them where the responsibility falls through.

Regulators are the obvious candidates, but their instruments are not shaped for the problem. The European Union's AI Act, which entered substantive force during 2025, classifies systems by risk and imposes obligations on developers and deployers. It does not require disclosure of cognitive carry-over effects to end-users, nor monitoring in deployment. The United Kingdom's pro-innovation framework prefers sector-specific guidance and avoids cross-cutting consent obligations. The United States, post the rescission of the Biden-era AI executive order in 2025, has effectively no federal framework at all.

Employers have a duty of care to employees and, in regulated sectors, a duty of care to clients and patients. That duty arguably already extends to an obligation to understand the cognitive risks that workplace tools might impose. The General Medical Council in the United Kingdom and equivalent professional bodies elsewhere have begun to issue guidance on AI use in clinical practice, but these documents focus overwhelmingly on data protection, accuracy and clinical accountability. They do not, at the time of writing, address acute carry-over.

Educators bear a different but related duty. The Department for Education's guidance and the curricular adjustments under way in many school systems are mostly oriented toward whether AI use degrades the development of skill over time. They do not address whether AI use during an assessment, or in the hour before one, materially changes what the assessment measures.

Platform vendors are commercially positioned to be most relevant and culturally positioned to be least. The major AI labs (OpenAI, Anthropic, Google DeepMind, Microsoft) have all published responsible-use guidance of varying depth, and have all engaged, with varying degrees of seriousness, with concerns about cognitive effects. None of them, at the time of writing, surfaces information about cognitive carry-over to end-users in the products themselves. The prevailing commercial logic, in which engagement and frequency of use are positive metrics, does not align with cognitive-risk disclosure, and there is no regulatory instrument forcing the alignment.

Individual users carry the residual responsibility, the way they carry it for every consumer product whose risks have been imperfectly disclosed. That is a thin reed to lean on in any sector where the user is, in fact, the patient or the student or the citizen rather than the operator of the tool.

The honest map of responsibility is therefore a sparse one. There is no regulator currently obliged to act, no employer currently obliged to act, no educator currently obliged to act, no platform currently obliged to act, and no user adequately positioned to act. The gaps are not bugs in the system; the system was not designed for the problem, because the problem was framed, until very recently, as chronic.

Replication, Caveats, And The Cost Of Waiting

It would be irresponsible to leave this account without flagging that the strongest version of the acute-impairment claim still rests on a small number of studies, much of it unreplicated and some of it not yet peer reviewed. The MIT preprint by Kosmyna and colleagues has the limitations its own authors acknowledged: a sample of 54 participants in a single geographic region, no peer review at the time of its initial release, and a fourth-session reassignment design that, while suggestive, is not definitive. The CHI 2025 paper by Lee and colleagues is a survey of self-reported behaviour, not a controlled experiment. The Gerlich 2025 paper in Societies is correlational and was subsequently corrected by the publisher in September 2025 for unrelated issues.

The reported April 2026 multi-institution study would be the strongest causal evidence yet, but its full methodological detail is not, at the time of writing, available for the kind of scrutiny that allows confident claims. It will need to be peer reviewed. It will need to be replicated. It will need to be tested against the standard battery of cognitive-experiment objections: demand characteristics, expectancy effects, the difficulty of isolating the AI-use intervention from time-on-task confounds, the question of whether the post-test deficit is a real cognitive change or a motivational artefact.

These caveats matter, and the article that elides them does the public no favours. They do not, however, license inaction. The asymmetry of the situation is consequential. The cost of acting on a finding that turns out to overstate its case is, mostly, the modest inconvenience of disclosure obligations that would have been good practice anyway. The cost of failing to act on a finding that turns out to be robust is the continued silent conversion of millions of high-stakes interactions into a field experiment whose subjects never agreed to participate.

The right posture, on the present evidence, is therefore neither alarm nor dismissal. It is the unfashionable posture of taking research seriously while it is still emerging, of treating disclosure and consent as prudent defaults under uncertainty, and of designing deployment contexts to be measurable, monitorable and reversible. None of these are dramatic interventions. None require believing that the strongest claims in the literature are true. They require only believing that they might be.

What The Evidence Demands, And What It Does Not

What the evidence demands is modest, and would be modest even if every study cited above were fully replicated and beyond serious dispute. It demands, first, that the deployment of AI assistants in high-stakes settings be accompanied by disclosure to the individuals whose welfare depends on the cognitive performance that follows. The patient is entitled to know that the clinician is using a scribe. The student is entitled to know that the teacher is grading with a marker. The benefits claimant is entitled to know that the case-worker has just closed a chatbot.

It demands, second, that organisations deploying these tools begin to monitor outcomes in a manner sensitive to acute carry-over. Quality-assurance audits exist; they have not, until now, been designed with the carry-over hypothesis in mind, but they could be without much trouble.

It demands, third, that regulators, professional bodies and educators begin to update their guidance with the acute frame in view, and stop treating the cognitive consequences of AI use as a problem of long-term skill development alone. It demands, fourth, that platform vendors stop pretending the question of cognitive effects is somebody else's department, and begin to surface, in their products, the relevant information that emerging research has produced.

What the evidence does not demand is panic. It does not demand that AI be removed from clinics, classrooms or public-service settings. It does not demand that workers stop using tools that, on net, help them do their jobs. It does not demand the kind of moral-panic legislation that would, if enacted on the present evidence, almost certainly do more harm than good.

What it asks of us is the harder thing: to live, as adults, in the uncomfortable middle ground where evidence is suggestive but not yet conclusive, where the costs of action are real but bounded, and where the costs of inaction are uncertain but potentially large. The history of technology regulation is mostly the history of arriving at this middle ground decades after the relevant tools have already reshaped the landscape. The unfashionable possibility, this time, is to arrive earlier.

The Smaller, Truer Claim

Strip the press coverage of its more lurid framings and what remains is a claim that is smaller and harder to dismiss. The claim is not that AI is rotting our brains. The claim is not that ten minutes of ChatGPT will leave you intellectually impaired for the rest of the day. The claim is not even that the acute effect, if it exists, is large enough to matter in the average use case.

The claim is that there is a measurable carry-over effect from recent AI use to subsequent unaided cognitive performance, that the effect appears on the order of minutes rather than years, that the existing deployment of AI in high-stakes contexts has not been designed with that effect in mind, and that the consent and disclosure infrastructure required to make that deployment ethically defensible has not been built. The reported April 2026 study strengthens the first proposition. The MIT, Microsoft, Carnegie Mellon and Swiss Business School literatures of the past eighteen months have already strengthened the second. The third is empirical and visible to anyone who looks. The fourth is a matter of public policy that we have, until now, declined to address.

The room from the opening of this article, the desk and the laptop and the EEG electrodes, is not a metaphor. It is a research site, one of a small but growing number, in which the cognitive tone of recent AI users is being measured against the cognitive tone of unaided controls. Whether the field finds the effect to be small and easily managed, or large and policy-relevant, will become clearer in the months ahead. That it is being measured at all is the first piece of good news. That the measurements are not yet, in any meaningful sense, being relayed to the patients, students, clients and citizens whose welfare depends on the unaided performance that follows the use of the tools, is the part of the situation that does not require any further evidence to fix.

The technology will not pause for the science to catch up. The disclosure can.

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References & Sources

1. Kosmyna, N., et al. (2025). Your Brain on ChatGPT: Accumulation of Cognitive Debt when Using an AI Assistant for Essay Writing Task. arXiv preprint arXiv:2506.08872. MIT Media Lab. https://www.media.mit.edu/publications/your-brain-on-chatgpt/
2. Lee, H.-P. H., et al. (2025). The Impact of Generative AI on Critical Thinking: Self-Reported Reductions in Cognitive Effort and Confidence Effects From a Survey of Knowledge Workers. Proceedings of the CHI Conference on Human Factors in Computing Systems (CHI '25), Yokohama. Microsoft Research / Carnegie Mellon University. https://www.microsoft.com/en-us/research/publication/the-impact-of-generative-ai-on-critical-thinking-self-reported-reductions-in-cognitive-effort-and-confidence-effects-from-a-survey-of-knowledge-workers/
3. Gerlich, M. (2025). AI Tools in Society: Impacts on Cognitive Offloading and the Future of Critical Thinking. Societies, 15(1), 6. SBS Swiss Business School. https://www.mdpi.com/2075-4698/15/1/6
4. Gerlich, M. (2025). Correction: AI Tools in Society: Impacts on Cognitive Offloading and the Future of Critical Thinking. Societies, 15(9), 252. https://www.mdpi.com/2075-4698/15/9/252
5. Sparrow, B., Liu, J., & Wegner, D. M. (2011). Google Effects on Memory: Cognitive Consequences of Having Information at Our Fingertips. Science, 333(6043), 776-778.
6. Maguire, E. A., et al. (2000). Navigation-related structural change in the hippocampi of taxi drivers. Proceedings of the National Academy of Sciences, 97(8), 4398-4403.
7. Javadi, A.-H., Spiers, H. J., et al. (2017). Hippocampal and prefrontal processing of network topology to simulate the future. Nature Communications, 8, 14652.
8. Department for Education (UK). (2025, June). Generative artificial intelligence (AI) in education: policy paper. https://www.gov.uk/government/publications/generative-artificial-intelligence-in-education
9. European Parliament and Council of the European Union. (2024). Regulation (EU) 2024/1689 (Artificial Intelligence Act). Official Journal of the European Union.
10. UK Department for Science, Innovation and Technology. (2023). A pro-innovation approach to AI regulation. White paper. HM Government.
11. TIME Magazine. (2025). ChatGPT's Impact On Our Brains According to an MIT Study. https://time.com/7295195/ai-chatgpt-google-learning-school/
12. WBUR News. (2025, September 16). Using ChatGPT as a homework tool? MIT researcher says think twice. https://www.wbur.org/news/2025/09/16/ai-study-essays-brain-cognition
13. Fortune. (2025, February 11). AI might already be warping our brains, leaving our judgment and critical thinking 'atrophied and unprepared,' warns new study. https://fortune.com/2025/02/11/ai-impact-brain-critical-thinking-microsoft-study/
14. Policy Options / IRPP. (2025, September). How AI is eroding human memory and critical thinking. https://policyoptions.irpp.org/2025/09/ai-memory/
15. Frontiers in Psychology. (2025). The cognitive paradox of AI in education: between enhancement and erosion. https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2025.1550621/full

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Tim Green UK-based Systems Theorist & Independent Technology Writer

Tim explores the intersections of artificial intelligence, decentralised cognition, and posthuman ethics. His work, published at smarterarticles.co.uk, challenges dominant narratives of technological progress while proposing interdisciplinary frameworks for collective intelligence and digital stewardship.

His writing has been featured on Ground News and shared by independent researchers across both academic and technological communities.

ORCID: 0009-0002-0156-9795 Email: tim@smarterarticles.co.uk

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On the morning of 18 March 2026, Deborah Leslie stood at the lectern of the Supreme Court of Georgia, in downtown Atlanta, and tried to explain why several of the cases in her brief did not exist. Leslie was an Assistant District Attorney with Clayton County, assigned to appellate work and assets forfeiture, and she had filed papers opposing a new trial for Hannah Payne, a young woman convicted in 2023 of the murder of Kenneth Herring after a hit-and-run on a Clayton County road in 2019. Payne, then twenty-five, was serving life with the possibility of parole. Her lawyer, Brian Steel, had filed for a fresh trial on the grounds that her original counsel had failed to ask the jury to consider citizen's arrest as a defence. The state's response, signed by Leslie, ran to dozens of pages. It cited authorities. The authorities, in many places, were imaginary.

Chief Justice Nels Peterson did not bury the point. From the bench, he counted aloud: at least five citations to cases that did not exist, and at least five more to cases that existed but did not say what Leslie's brief claimed they said. The video of the exchange, which would later be viewed more than five million times across various clips, has the strangled politeness of a hearing that everyone in the room knows is going badly. Leslie initially suggested the citations might have been added to the version filed with the court rather than the one she had drafted. Peterson noted that the same non-existent cases appeared in the brief opposing Payne's motion below. The implication was unavoidable. The phantom citations were hers.

A week later, on 27 March, Clayton County District Attorney Tasha Mosley wrote to the Chief Justice. The letter, published shortly after by local outlets, conceded what was already obvious. Leslie had used artificial intelligence to draft the filing. She had not verified the output. The office had moved against her: a grievance with the State Bar of Georgia, suspension, a performance improvement plan, loss of privileges. In her own affidavit, Leslie said the errors were not intentional and that the references “were not independently verified before inclusion.” The Hannah Payne appeal, a case with a victim's family, a defendant on a life sentence, and a contested constitutional argument about the right to effective counsel, had been compromised by language a model invented in a few seconds at no cost.

The Georgia incident is not anomalous. It is the latest, most public entry in a list that legal scholar Damien Charlotin, who divides his time between Sciences Po Law School and HEC Paris, has been building since April 2025 in a database he started because he could not find anyone else doing the work. By the spring of 2026, his AI Hallucination Cases tracker had passed 1,200 documented incidents from courts around the world, with roughly 800 from the United States alone. On a single day in March 2026, he logged seventeen. The rate, Charlotin has said, is still rising. What began as a curiosity in late 2022, when ChatGPT first leaked into the workflows of overworked solicitors and overconfident litigants, has become a structural feature of contemporary legal practice. The machine is in the building. The machine lies. Sometimes the lies get caught. Sometimes they do not.

Lay this fact alongside another, less visible one. According to the Legal Services Corporation's 2022 Justice Gap study, conducted with NORC at the University of Chicago, ninety-two per cent of the substantial civil legal problems experienced by low-income Americans receive no, or insufficient, legal help. Seventy-four per cent of low-income households face at least one such problem in any given year. In England and Wales, Ministry of Justice statistics for the third quarter of 2025 showed that fifty-nine per cent of civil cases in the County Court involved at least one party with no legal representation. In state civil dockets across the United States, self-representation rates routinely exceed ninety per cent in housing, family, and consumer cases. The justice gap is not a metaphor. It is the operational reality of most non-criminal courtrooms in the English-speaking world.

This is the contradiction at the heart of the moment. Generative AI is the only piece of legal infrastructure that has, in living memory, become cheaper and more widely available rather than more expensive and more rationed. For the unrepresented mother fighting an eviction, the asylum seeker filling in a witness statement at midnight, the small employer hit with a discrimination claim, a free large language model is, on its worst day, more responsive than the legal aid hotline that has not picked up in three hours and, on its best day, capable of producing a coherent draft of a defence. The same technology, deployed by a tired prosecutor in a county DA's office or a partner under deadline at a magic-circle firm, can introduce phantom precedent into the foundations of a criminal appeal. AI is simultaneously democratising access and corrupting the evidentiary substrate. There is no clean way to keep one without the other.

The Hallucination Problem, Precisely

It helps to be technical about what is happening, because the loose language around “AI mistakes” understates the issue. A large language model does not retrieve. It predicts. Given a prompt, it generates the most statistically plausible next token, then the next, conditioned on its training data and on whatever it has just produced. When the prompt is “cite a case supporting the proposition that an officer's mistaken belief in probable cause is reviewed for objective reasonableness”, the model produces something that looks like a citation, because in the training data the answers to such prompts are followed by things that look like citations. Volume number, reporter, page, year, parenthetical court abbreviation. The format is the easy part. The model has internalised the format. What it has not internalised is the existence of the case.

This is why the hallucinations are so dangerous. They are not random. They are formally correct. A fabricated case will have a plausible volume number for the reporter, a sensible district, a year that lines up with the legal doctrine being argued, and often a holding that maps onto the proposition being supported. The fabrication is grammatical. The citation, considered in isolation, is indistinguishable from a real one until someone looks it up. The Stanford RegLab's preregistered study by Varun Magesh and Faiz Surani, published in the Journal of Empirical Legal Studies, gave the phenomenon a metric: even legal-specific tools hallucinated at startling rates. Westlaw's AI-Assisted Research generated incorrect or fabricated information thirty-three per cent of the time in their tests. LexisNexis's Lexis+ AI hallucinated seventeen per cent of the time. Thomson Reuters' Ask Practical Law AI sat near the same number. Premium products. Trained on real case law. Marketed to professionals. Still inventing.

The roll call of incidents starts with Mata v Avianca, the Manhattan personal-injury suit against the Colombian airline that became the founding text of the genre. In June 2023, Judge P. Kevin Castel of the Southern District of New York imposed sanctions of $5,000 on attorneys Steven Schwartz and Peter LoDuca, and on the firm Levidow, Levidow & Oberman, after Schwartz used ChatGPT to research a brief that ended up citing six cases that did not exist: Varghese v. China South Airlines, Martinez v. Delta Airlines, Shaboon v. EgyptAir, Petersen v. Iran Air, Miller v. United Airlines, and Estate of Durden v. KLM Royal Dutch Airlines. When opposing counsel pointed out that the cases could not be found, Schwartz had asked ChatGPT whether they were real; the model assured him they were and produced fabricated full texts. He had been a member of the New York bar since 1991. “It just never occurred to me”, he testified, “that it would be making up cases.”

Then came the parade. In late 2023, Michael Cohen, the former personal lawyer to Donald Trump, sent his attorney three citations he had pulled from Google's Bard, all fabricated, in support of a motion for early termination of supervised release. The judge declined to sanction Cohen but called the episode “embarrassing and certainly negligent”. In Texas, in November 2024, Judge Marcia Crone of the Eastern District sanctioned Brandon Monk in Gauthier v. Goodyear Tire & Rubber Co. after a brief produced with the help of Anthropic's Claude cited authorities that did not exist. In June 2025, the High Court of England and Wales handed down its joined judgment in Ayinde v London Borough of Haringey and Al-Haroun v Qatar National Bank QPSC, a decision that read less like a routine ruling and more like a public warning. The grounds for review in Ayinde, drafted by a barrister called Ms Forey, misstated section 188(3) of the Housing Act 1996 and cited five non-existent cases, including a phantom “El Gendi v Camden LBC”. In Al-Haroun, a solicitor's witness statement contained eighteen authorities that did not exist, with others misquoted or inapplicable, after the solicitor relied on his client's research without verifying it. The Divisional Court was blunt: GenAI does not extinguish professional responsibility, and Rule 11 equivalents in England and Wales apply with full force regardless of whether a human or a model produced the text.

Australia has produced its own running list. On 19 July 2024, before Justice Amanda Humphreys in Victoria, a solicitor in a marital dispute submitted a list of “relevant” prior cases that turned out to have been generated by AI. He became, that year, the first Australian lawyer formally sanctioned for AI-generated fabrications. He was barred from practising as a principal and required to work under supervision for two years. In August 2025, before the Supreme Court of Victoria, defence lawyer Rishi Nathwani, KC, apologised to Justice James Elliott for filing submissions in a teenager's murder trial that included fabricated quotes from a speech to the state legislature and non-existent citations purportedly from the same court. The errors caused a twenty-four-hour delay; Elliott eventually ruled the youth not guilty of murder by reason of mental impairment, but the embarrassment to the bar was complete. In the months that followed, a Western Australian solicitor was referred to that state's regulator for tendering documents citing four cases that either did not exist or were misreferenced.

South Africa joined the parade in 2025. In Mavundla v MEC: Department of Co-Operative Government and Traditional Affairs KwaZulu-Natal, the KwaZulu-Natal High Court found that of the nine authorities Mavundla's legal team had cited, only two were real. Among the fabrications was a confidently asserted “Hassan v Coetzee”, complete with a citation, a court, a year, and a tidy doctrinal proposition, none of which corresponded to any actual case. The court referred Mavundla's lawyers to the Legal Practice Council for investigation and ordered them to bear the costs of a hearing in which an inordinate amount of judicial and counsel time had been spent searching for cases that were never going to be found. The Cliffe Dekker Hofmeyr alerts that catalogued the affair noted, drily, that good intentions and apologies were no longer mitigation. They were table stakes.

Charlotin's tracker captures the cumulative shape. The early cases were almost all lawyers. By 2025, the share of pro se litigants caught submitting fabricated citations had grown sharply; Bloomberg Law reported that at least twenty-four self-represented litigants in the United States had been hit with monetary sanctions for AI-generated filings in the eighteen months following the second half of 2023. The trend in the data is unmistakable. The technology is not going away. The hallucinations are not going away. Adoption is outpacing verification, and the courts are catching up by issuing sanctions and warnings rather than by deploying any meaningful screening.

The Other Side of the Same Coin

Now consider who else is using these systems, and why. The New York State Bar Association published a piece on 10 February 2026 by its Pro Se Advocacy interest section titled “Pro Se Advocacy in the AI Era: Benefits, Challenges, and Ethical Implications”. The article does not pretend to resolve the contradiction. It frames it. It catalogues the practical uses to which an unrepresented person might put a chatbot: drafting letters to the court, preparing a defence to a parking ticket, navigating procedural requirements that the court itself communicates through forms a non-lawyer cannot reliably parse. It also notes the obvious risk: hallucinations that look like citations, advice that looks like guidance, and a tool that the client cannot themselves audit. The piece poses the question that the legal profession has, until very recently, been allowed not to answer: “Are the people, who otherwise would not have legal counsel, better served by at least having a chatbot to assist them?”

Similar commentary has come out of South Africa and Australia in the same window. The South African Daily Maverick ran a piece in July 2025 arguing that AI hallucinations were threatening the administration of justice in the country, while simultaneously acknowledging that the country's own access-to-justice gap, particularly in family and labour matters, had created a population for whom no realistic alternative to AI-mediated self-help existed. In one widely cited case, a self-represented litigant called Mr Makunga drafted heads of argument with the help of AI tools and online research, and the presiding judge commended the quality of his submissions, noting that some members of the practising bar had filed worse arguments than the AI-augmented ones. The South African legal profession is in the position of warning the public against the same technology that is, for many of those same members of the public, the only legal-adjacent help on offer. Australian commentators have made the same point, often more sharply: that decades of cuts to legal aid have produced a country where AI is not a luxury for the poor litigant but the default.

The numbers confirm what the rhetoric implies. The 2022 Justice Gap report from the Legal Services Corporation, the federally funded body responsible for funding civil legal aid in the United States, found that ninety-two per cent of the civil legal problems faced by low-income Americans received either no help or not enough. In 2021, LSC grantees were unable to provide adequate help on roughly 1.4 million of the 1.9 million problems brought to them. Across state civil courts, the New York City Bar Association has called the gap a “chasm”. In England and Wales, the Ministry of Justice's own statistics for July to September 2025 recorded that fifty-nine per cent of County Court civil cases involved an unrepresented party. In housing matters, in family proceedings, and in claims under £10,000, the proportion is higher still. The legal profession has been priced out of the lives of the people whom the legal system most often touches.

For those people, generative AI is not a fancy productivity tool. It is the only piece of legal infrastructure that scales to their need. A free model that responds in seconds, drafts in plain English, and produces something resembling a coherent argument is more meaningful in the life of an evicted tenant than a thirty-page government leaflet, a legal aid waiting list of nine months, or a self-help kiosk staffed by a volunteer who can offer information but not advice. The bar associations know this. They are also writing the practice notes that make their members liable for AI-generated errors. The result is a regulatory regime that, on paper, treats AI as a hazardous tool that licensed professionals must approach with caution, while in practice the same tool is being used as a substitute for those professionals by people the profession does not serve.

That asymmetry is not just uncomfortable. It is dangerous. When a lawyer files a brief with phantom citations, the lawyer is sanctioned, the judge is annoyed, the client may suffer reputational damage, and the firm pays the bill. Friction is built into the relationship. The lawyer has insurance, a regulatory body, a duty of competence. When a pro se litigant files the same brief, none of those scaffolds exist. The litigant is told, sometimes for the first time in their interaction with the system, that they have submitted falsehoods to a court. Their case is dismissed, or worse. Their credibility, which they did not choose to risk, is lost. They have no insurer, no body to pay sanctions, no firm to absorb the loss. They have a chatbot and the consequences.

Risk and Where It Lands

The doctrinal answer to “who bears the risk” is easy to state and brutal to apply. In every jurisdiction that has confronted the question, the answer has been: whoever signed the filing. Rule 11 of the United States Federal Rules of Civil Procedure binds the lawyer or the unrepresented party to the truth of every assertion. The Civil Procedure Rules in England and Wales impose comparable duties. The Legal Practice Council in South Africa has already announced that good intentions are not mitigation. Australian state bars have made the same point. The doctrinal posture is that the human is the author, the AI is the tool, and the tool's errors are the author's problem.

This makes intuitive sense for the represented client and their lawyer. It is much harder to defend in the case of the unrepresented. A pro se litigant who copies a fabricated case from a chatbot has not been negligent in the way a lawyer has been negligent. The lawyer is a trained professional with a duty of competence and an obligation to know that ChatGPT does not search; the litigant is a person who can read English and has been given a search box. The same conduct, on the same facts, attracts the same legal exposure but reflects radically different fault. Sanctions imposed on a pro se litigant for AI-generated falsehoods land on someone whose alternative was not better legal advice; their alternative was no advice at all. The system tells them, in effect, that they should have known not to use the tool that the system also will not give them an alternative to.

There are emerging cases that test the edges of this rule. On 4 March 2026, Nippon Life Insurance Company of America filed suit in the Northern District of Illinois against OpenAI Foundation and OpenAI Group PBC, alleging that ChatGPT, used by an opposing pro se litigant, had engaged in tortious interference with a settled contract, abuse of process, and the unlicensed practice of law. The Nippon complaint is one of the first attempts to push a portion of the risk back upstream, onto the maker of the tool, rather than letting it rest entirely on the user. It is far from clear whether the case will survive a motion to dismiss, and the substantive merits are contested, but the move is intellectually significant. If a chatbot purports to give legal advice to a litigant, and the advice is wrong, and the litigant's reliance produces real harm to a counterparty, then liability somewhere in that chain is unavoidable. The question is whether it stops at the user, where current doctrine puts it, or extends to the model, the deployer, or the platform.

State legislatures have begun to nibble at the same question. New York legislators are considering a bill that would expressly make companies liable for the unauthorised practice of law by their AI chatbots. The premise is that a tool that confidently advises a non-lawyer on the contents of a defence is, functionally, practising law without a licence; the licensing regime exists for a reason; and the licensing regime should bind the company that deploys the tool. The counter-argument, made vigorously by the deployers, is that disclaimers are visible, that the tool is a general-purpose system, and that holding the platform liable will simply shut the tool down for the very people who most need it. The argument is real on both sides. It is also, to borrow a WIRED instinct, a debate that exists because the legal system has refused to fund civil representation at the level the population requires.

The Patchwork of Rules

What courts have done in the meantime is improvise. Judge Brantley Starr of the Northern District of Texas issued the first published standing order on AI in court filings in 2023, requiring attorneys to certify either that no portion of the filing was drafted with generative AI or that any AI-drafted portion had been independently verified by traditional means. Filings without the certificate would be stricken. Starr's order travelled. By the end of 2025, Bloomberg Law's tracker had logged hundreds of standing orders, general orders, and local rules across federal and state courts in the United States addressing AI use in submissions. The orders are not uniform. Some require disclosure of the model used. Some require certification of independent verification. Some prohibit AI in particular categories of filing. Some are silent on pro se litigants and silent in different ways on legal aid clinics that use AI in supervised work.

In the United Kingdom, the Bar Council and the Solicitors Regulation Authority have issued guidance, and the Lord Chief Justice's office has updated its own guidelines for judges on the use of AI tools. The Ayinde judgment did most of the doctrinal work: lawyers are professionally responsible for everything they sign, AI cannot be invoked as an excuse, and serious cases will be referred to the regulators. In Australia, the Victorian Legal Services Board has begun to issue conditions on practising certificates for solicitors caught with fabricated citations. The South African Legal Practice Council has confirmed that referrals for AI-generated fabrications will be standard. None of these regimes is coordinated with the others. None deals systematically with the unrepresented litigant. All of them assume that the deterrent function of sanctions is sufficient, even though the data Charlotin is collecting suggests that sanctions are not, in fact, slowing the rate of submissions.

There is a distinct strand of proposal that goes beyond after-the-fact sanction. The most robust version is the “hyperlink rule” advocated in legal-technology circles, which would require every authority cited in a filing to be backed by a working hyperlink to the actual case in a recognised public database, with verification carried out before submission. Some jurisdictions have flirted with the idea. None has imposed it as a hard rule, in part because the doctrinal infrastructure for stable case URLs is patchy and in part because requiring hyperlinks puts an additional procedural burden on litigants who already cannot navigate the existing forms. A weaker version is the retrieval-augmented generation (RAG) requirement, in which AI tools used in legal practice must ground their outputs in a curated, court-vetted database of authorities rather than in the open internet. Westlaw, LexisNexis, and Thomson Reuters all market RAG-based products. The Stanford RegLab study showed that those products still hallucinate, just less often. RAG is mitigation, not solution.

A more interesting proposal, surfacing in academic work and in the most thoughtful sections of the New York State Bar Association's pro se commentary, is a two-tier disclosure regime. Lawyers using AI face one set of rules: they must disclose, certify, and verify, and they will be sanctioned if they fail. Pro se litigants face a different set: they must disclose, but the court will treat AI-generated errors as a procedural defect that triggers an opportunity to correct, not a sanctionable falsehood, provided the litigant did not knowingly file material they suspected to be fabricated. The justification is that the unrepresented litigant has a different epistemic position. They were not supposed to know. The system that did not give them a lawyer cannot then sanction them for the only substitute available. The objection is that the rule creates a second-class evidentiary regime in which the truth of submissions depends on who made them, and that asymmetry is its own injustice.

The Hardest Cases

It is worth sitting with the kinds of cases where this matters most. The Ayinde claimant, on whose behalf phantom cases were cited, had a real housing problem. The barrister's failure did not invent the homelessness. It complicated the record on which the homelessness would be adjudicated. In Mavundla, a real dispute about traditional leadership was filed alongside fabricated authorities, and the case was referred to the Legal Practice Council in part because the court could not separate the genuine claim from the contaminated argument. In the Hannah Payne appeal, the constitutional question, whether her trial counsel had been ineffective in failing to present a citizen's arrest defence, is genuine and consequential. Leslie's hallucinated brief did not change the facts of the underlying killing. It changed the texture of the appellate record, made the prosecution's argument less credible, and forced the Georgia Supreme Court to spend its time policing the inputs rather than weighing them.

For the unrepresented, the most painful version of the problem is not the high-stakes appeal. It is the small case that was always going to be hard. A tenant in Manchester or Atlanta or Cape Town files a defence to an eviction. The defence cites cases that do not exist. The landlord's counsel files a reply that catches the fabrication. The judge, depending on jurisdiction, either strikes the defence or grants it grudging weight. The tenant loses the home that they were trying to keep, in part because the only legal help they could afford was a model that lied to them. The fault lies, on every doctrinal account, with the tenant. The injury, on any honest account, is on the tenant and the tenant's children.

That kind of case rarely makes the Charlotin database. It does not produce a published opinion. It does not generate a sanctions order. It generates a default judgment, a removal, a debt. Some portion of the court orders that are entered against unrepresented defendants in the United States and the United Kingdom in 2025 and 2026 are now, almost certainly, downstream of AI-generated filings that were never identified as such because no one in the courtroom had time or expertise to check. The dark figure of AI's contribution to the justice gap is, by definition, invisible. The cases we know about are the ones in which someone, on the other side, had the resources to look up the citations. Where there is no other side with resources, there is no audit. Where there is no audit, there is no record.

What Should Be Done

This article will not pretend that there is a clean fix. WIRED's instinct, properly, is to take a position rather than to nod sympathetically at every party. The position is this. The legal profession's current posture, in which sanctions land on the human signatory regardless of context and AI tools are treated as neutral hazards, is intellectually consistent and morally untenable. It works only if the system also funds the legal representation that would let people avoid the hazardous tool. It does not. The same legislatures and bar associations writing tighter AI rules have, for thirty years, allowed civil legal aid to be eviscerated. They cannot now have it both ways.

There are concrete steps. Court-vetted, publicly funded retrieval-augmented systems, designed specifically for unrepresented litigants in jurisdictions where self-representation is the norm, would meaningfully reduce hallucination rates and shift some risk back to public infrastructure where it belongs. The technology exists. The cost is a fraction of what jurisdictions spend on courthouse construction. The political will is the obstacle. Two-tier disclosure regimes, in which courts adopt different sanctions postures for represented and unrepresented filers, would acknowledge the moral asymmetry the current rules ignore. Mandatory hyperlinking of authorities, with court-side automated verification, would let scale solve a scale problem. Liability that extends, where appropriate, to the deployer of a model marketed as a legal assistant, would give platforms an incentive to invest in accuracy that disclaimers do not.

None of these will fix the underlying issue, which is that justice is expensive and most people cannot afford it. Generative AI has revealed that fact in a particularly acute way: the cheapest available legal counsel is also the least reliable, and the most reliable counsel has never been cheap. Treating AI as either saviour or saboteur misses the structure of the problem. The technology is a mirror. It reflects, with terrifying efficiency, both the procedural form of legal argument and the unwillingness of states to fund the substance. The Georgia prosecutor and the Manchester tenant are using the same tool for related reasons: their respective systems have not given them what they need to do their work.

Phantom precedent is what happens when a pattern-matching machine is asked to do the job of a research lawyer. It is also, more deeply, what happens when courts pretend that everyone in front of them has equal access to the means of producing reliable arguments. They do not. They have never. The arrival of AI has made that gap visible in a new way, and the next decade of legal regulation will be measured by whether courts and legislatures respond to the visibility or to the symptom. If the answer is more sanctions, more warning notices, and more standing orders, the gap will widen. If the answer is funded counsel, vetted public tools, and a doctrinal reckoning with who actually bears the risk when a model lies, there is at least a route through. The contradiction will not resolve itself. Someone has to choose.

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References

1. CBS News Atlanta, “AI in Georgia courts raises new questions after Clayton County prosecutor admits citing fake cases”, 2026.
2. Atlanta News First, “Chief justice: Attorney cites nonexistent cases in opposing new trial for woman convicted of murder”, 23 March 2026.
3. Atlanta News First, “Attorney with Clayton County DA's Office apologizes for using AI, citing fake cases in court brief”, 30 March 2026.
4. Damien Charlotin, AI Hallucination Cases Database, damiencharlotin.com/hallucinations, accessed April 2026.
5. Eugene Volokh, “In One Day (Mar. 31), 17 U.S. Court Decisions Noting Suspected AI Hallucinations in Court Filings”, Reason, 6 April 2026.
6. New York State Bar Association, “Pro Se Advocacy in the AI Era: Benefits, Challenges, and Ethical Implications”, 10 February 2026.
7. Mata v. Avianca, Inc., 678 F. Supp. 3d 443 (S.D.N.Y. 2023), Opinion and Order on Sanctions, Judge P. Kevin Castel, 22 June 2023.
8. NPR, “Michael Cohen says he unwittingly sent AI-generated fake legal cases to his attorney”, 30 December 2023.
9. Gauthier v. Goodyear Tire & Rubber Co., E.D. Tex., Judge Marcia Crone, sanctions order, November 2024.
10. Ayinde v London Borough of Haringey; Hamad Al-Haroun v Qatar National Bank QPSC and QNB Capital LLC, [2025] EWHC 1383 (Admin), judgment of the Divisional Court, June 2025.
11. Information Age (Australian Computer Society), “First Australian lawyer penalised for AI blunder”, 2025.
12. NBC News, “Australian lawyer sorry for AI errors in murder case, including fake quotes and made up cases”, 15 August 2025.
13. Mavundla v MEC: Department of Co-Operative Government and Traditional Affairs KwaZulu-Natal and Others, [2025] ZAKZPHC 2.
14. Cliffe Dekker Hofmeyr, “Another episode of fabricated citations, real repercussions: South African courts show no tolerance for AI-hallucinated cases”, 4 July 2025.
15. Daily Maverick, “AI 'hallucinations' are threatening the administration of justice in South Africa”, 15 July 2025.
16. Varun Magesh and Faiz Surani et al., “Hallucination-Free? Assessing the Reliability of Leading AI Legal Research Tools”, Journal of Empirical Legal Studies, Stanford RegLab, 2024.
17. Legal Services Corporation, The Justice Gap: The Unmet Civil Legal Needs of Low-Income Americans, 2022 Report, NORC at the University of Chicago.
18. Ministry of Justice (England and Wales), Civil Justice Statistics Quarterly, July to September 2025.
19. New York City Bar Association, “The Justice Gap Has Become a Chasm”.
20. Bloomberg Law, “Federal Court Judicial Standing Orders on Artificial Intelligence” comparison table, 2025.
21. Legal Dive, “Federal judge seeks to prevent generative AI mistakes in briefs”, 2023, on Judge Brantley Starr's standing order.
22. Vermont Law Review, “Mandatory AI Disclosures: Enforcing A Uniform Standard”, 2025.
23. National Law Review, “Preventing Fabricated AI Legal Authorities: The Case for a Mandatory 'Hyperlink Rule'“.
24. Bloomberg Law, “Big Law Grapples With AI-Fueled Pro Se Surge, Rising Legal Costs”, 2025.
25. Georgetown Journal of Legal Ethics, “GPT, Esquire: How the Nippon Case May Shape the Future of AI in Pro Se Litigation”, 2026.

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Tim Green UK-based Systems Theorist & Independent Technology Writer

Tim explores the intersections of artificial intelligence, decentralised cognition, and posthuman ethics. His work, published at smarterarticles.co.uk, challenges dominant narratives of technological progress while proposing interdisciplinary frameworks for collective intelligence and digital stewardship.

His writing has been featured on Ground News and shared by independent researchers across both academic and technological communities.

ORCID: 0009-0002-0156-9795 Email: tim@smarterarticles.co.uk

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There is a particular kind of professional disappearance that does not show up in the unemployment figures. The illustrator still has a desk. The translator still has a website. The session musician still owns a violin. None of them have been fired. None of them have been informed, in any official capacity, that their occupation is being phased out. Their names remain on the same freelance registers, the same union rolls, the same tax filings as last year. And yet, quietly, almost imperceptibly at first, the floor underneath their work has begun to give way.

This is the strangest economic story of the decade, and it is unfolding without a moment of high drama. There are no factory closures, no mass layoffs, no town-square photographs of redundant workers carrying cardboard boxes. Instead, there is a slow, grinding compression of the rates a cover illustrator can charge for a magazine commission, a slight but stubborn drop in the volume of subtitling work coming through the agency in São Paulo, a pause in the email chain from the German publisher who used to commission a translator in Lagos every six months. Each individual moment is deniable. Taken together, they describe a structural rearrangement of the creative economy that the existing policy vocabulary, fixated on automation and job displacement, is not equipped to name.

UNESCO's flagship report on creativity and digital transformation, published on 18 February 2026 as the fourth edition of its Re|Shaping Policies for Creativity series, attempted to put a number on the rearrangement. Drawing on data from more than 120 countries, the report projected that creators worldwide are on course to lose up to 24 per cent of their revenues by 2028 as a direct consequence of generative AI, with music creators bearing the heaviest exposure and audiovisual creators close behind. An accompanying analysis published the same week by Inter Press Service amplified the geographical dimension of the problem, observing that the income losses are falling most heavily on freelance and self-employed creators in the global south, layering a new digital injury on top of long-standing inequalities in the cultural economy.

The numbers are striking, but they are not the most interesting part of the story. The most interesting part is the mechanism. This is not, in the conventional sense, a tale of automation. The translator working from Yoruba to Spanish has not been replaced by a translation engine in any specific role. She is still the translator on her own letterhead. What has happened is that the demand curve she used to live on has been displaced, almost overnight, by something that produces an approximate substitute for her output at near-zero marginal cost. The publishers, the production companies, the marketing agencies who used to commission her have not declared that they are switching to machines. They have simply stopped commissioning at the same volume, or they have begun negotiating from a position that assumes her labour competes with a free alternative. There is no transition point. There is no redundancy notice. There is no clean moment at which the policy concept of retraining becomes applicable, because the person is still doing the job. It is the job, as a paid activity, that is being hollowed out.

This distinction matters, and not only as semantics. The entire architecture of late twentieth-century labour policy, from unemployment insurance to active labour market programmes, was built on the assumption that economic dislocation comes with a clear event horizon. Someone is hired. Someone is laid off. Someone is retrained. Someone is rehired. Generative AI breaks the model not by accelerating the cycle but by detaching the harm from any of these events. The freelance writer is never laid off because she was never on a payroll. The illustrator does not get a redundancy package because there was no employer. The market she sold into has simply contracted, and the policy instruments designed to catch falling workers were not built to catch a falling market.

If the diagnosis is right, then the question that follows is the one the UNESCO report, the IPS News analysis, and a fast-growing literature on AI and the creative economy have all begun to circle. What policy instruments, beyond copyright reform, can address the harm? And who, in any meaningful sense, is responsible for the structural losses already under way?

The market, not the job, is the unit of analysis

To understand why this matters, it helps to look at what the existing debate is mostly about. Almost every legal and political fight currently being waged on behalf of creative workers concerns the upstream side of the AI value chain: whether AI labs should be allowed to train models on copyrighted works without permission, whether they should pay licensing fees for ingesting them, whether opt-out registers should be opt-in, whether the European Union's text-and-data-mining exception should narrow or expand. These are real and important fights. The Copyright Licensing Agency in the United Kingdom announced its Generative AI Training Licence to allow collective compensation for ingestion. The US Copyright Office has explored extended collective licensing on the Danish model. The Court of Justice of the European Union held its first hearing on generative AI and copyright in March 2026 in Like Company v Google, a case that may reshape the press publishers' right across the bloc.

Yet copyright, in any of its forms, only addresses one half of the harm UNESCO identified. The CISAC global economic study published in late 2024, conducted by the consultancy PMP Strategy on behalf of the international confederation of authors' societies, was unusually clear about this. The losses creators face split into two distinct streams. The first stream is the value of their existing works being scraped into training data without consent or remuneration. Copyright reform, however imperfectly, is built to address that. The second stream is the substitution effect: AI-generated outputs competing in the market against human-made works, depressing rates and shrinking commissions. Copyright, as currently understood, has very little to say about that second stream. Even a perfectly negotiated training licence does not change the fact that, once the model is trained, the marginal cost of producing a passable cover illustration falls towards zero, and the rate the illustrator can charge falls with it.

This is the harder problem, and it is the one to which the policy debate is only beginning to turn. The question is no longer simply how to compensate creators for the use of their work in training. It is how to sustain a market for human creative labour at all, when the marginal product of that labour can be approximated, however crudely, by a system that does not pay rent, sleep, or eat.

A taxonomy of instruments beyond copyright

The good news, if it can be called that, is that the policy toolkit available to address market collapse is broader than the copyright debate sometimes suggests. The bad news is that almost every instrument involves redistributing money from somebody who currently does not pay to somebody who currently does not receive, and the political economy of that redistribution is brutal.

AI cultural levies

The most direct proposal, and the one that has gained the most traction in European policy circles over the past year, is a levy on AI systems pegged to their commercial use of human cultural output. Arthur Mensch, the chief executive of the French AI lab Mistral, surprised many observers in 2025 when he publicly endorsed a revenue-based levy of roughly 1 to 1.5 per cent on commercial providers placing AI models on the European market, with funds channelled into a central pot to support cultural creation. The Mistral proposal is hardly altruistic; it would also, conveniently, harden a continental moat against American and Chinese model providers. But its underlying logic is sound, and it draws on a legal heritage that goes back six decades.

The German Copyright Act of 1965 introduced the first private copying levy, attaching a small charge to the cost of devices and media that allowed users to duplicate protected works. The principle was that where copying is structurally uncontrollable, levy-funded compensation, distributed by collective management organisations, is a more workable alternative than litigation. Generative AI presents an almost perfect analogue. The training and inference of a foundation model, at any meaningful scale, is structurally beyond the reach of one-by-one licensing. A statutory levy on commercial AI services, collected by reformed collective management organisations and distributed to creators on a metered basis, would close the substitution-side gap that copyright cannot reach. It would also avoid the worst pathology of contemporary copyright reform, which is that platforms can outspend rights holders in any line-by-line negotiation.

There are real objections. A levy must be set high enough to matter and low enough not to suppress useful applications. It must be administered by institutions trusted by both creators and developers, which is not how the existing collective management landscape is universally regarded. And it must avoid becoming a moat for incumbents who can absorb a 1.5 per cent levy more easily than a research lab in Nairobi or a co-operative model in Buenos Aires. None of these objections is fatal. All of them require institutional design rather than policy retreat.

Statutory remuneration rights

A close cousin of the levy approach is the statutory remuneration right, which decouples permission from payment. Under such a regime, AI developers might be permitted to train on lawfully accessible works without negotiating individual licences, but they would owe a non-waivable payment to authors through a collective body. The European Parliament's commissioned study on generative AI and copyright, published in 2025, examined this possibility in detail. Springer Nature's International Review of Intellectual Property and Competition Law has run a series of analyses, by scholars including Christophe Geiger, arguing that a statutory remuneration right grounded in fundamental rights to participate in cultural life could be the most workable foundation for a new compact.

The advantage of a statutory remuneration right over a pure levy is that it sits more comfortably within the existing copyright framework. The disadvantage is that it still ties payment to the use of identifiable works, which means it primarily addresses the ingestion side rather than the substitution side. Combined with a levy, however, it begins to look like a serviceable architecture.

Public commissioning and the Irish experiment

While the levy debate continues, a quieter experiment has been running in Ireland since 2022. The Basic Income for the Arts scheme, originally a three-year pilot, paid 2,000 randomly selected artists 325 euros a week, regardless of output. The Irish Department of Culture, Communications and Sport opened applications for the 2026 to 2029 successor scheme in April 2026, and a published cost-benefit analysis found that for every euro invested, society received a return of 1.39 euros, a number that has been disputed in the Irish press but has not been seriously dislodged.

The Irish scheme is not a response to AI. It was designed to address the chronic under-monetisation of cultural work in a digital economy that had already eroded the freelance commercial base before generative models arrived. But its logic transfers cleanly. If the market for creative output is being structurally compressed by a technology whose externalities are not internalised, then a state instrument that decouples income from market success becomes more, not less, defensible. A universal creative income, scaled to the working population of practising artists in any given country, would stand to working creatives roughly as agricultural support payments stand to small farmers facing global commodity competition. It is unromantic, slightly bureaucratic, and precisely the kind of thing that has historically allowed cultural production to survive market shocks.

The political objection is that it looks like a cultural welfare state. The substantive objection is that, depending on how it is administered, it can entrench the credentialing power of arts councils and reproduce existing gatekeeping. Both are genuine. Neither is decisive against an instrument that, in Ireland at least, has empirical results to its name.

Public procurement as creative-economy policy

A surprisingly underused lever is the purchasing power of governments themselves. Public sector bodies are, in aggregate, among the largest commissioners of design, illustration, translation, audiovisual production, and music in most economies. The US General Services Administration's draft AI procurement clause, the December 2025 OMB memorandum M-26-04, the United Kingdom's Procurement Policy Note 017 from February 2025, and California's executive order on AI vendor certification signed by Governor Gavin Newsom in April 2026 all introduce disclosure obligations for AI-generated content within government contracts. None of them, however, goes the further step of creating a procurement preference for human creative work in cultural production funded by public money.

A modest reform would be to require, for example, that any public broadcaster, national museum, ministry of culture, or city government commissioning creative output beyond a defined threshold use human creators where reasonably possible, with transparent disclosure when generative tools are used. This costs the state more, in the short term, than letting procurement officers chase the cheapest bid. It also creates a stable demand floor for working creatives and signals, with the kind of clarity that markets respond to, that public money will not be deployed to accelerate the collapse of the freelance creative class. India's labelling thresholds for AI-generated visual and audio content, and the EU AI Act's transparency requirements, are early sketches of the disclosure architecture this would require.

Collective bargaining as economic infrastructure

The 2023 agreements between the Writers Guild of America, the Screen Actors Guild and the Alliance of Motion Picture and Television Producers are, for all their imperfections, the most concrete demonstration that collective bargaining can produce workable rules around AI in creative work. The WGA contract specifies that AI-generated material cannot be considered literary material for credit purposes and gives writers the right to refuse to use AI tools, while preserving their ability to challenge the use of writers' work to train AI. The SAG-AFTRA contract distinguishes digital replicas of identifiable performers from synthetic performers built from no individual likeness, and creates compensation and consent obligations around both.

These provisions are not perfect. The 2024 SAG-AFTRA video game performer strike, which ran for many months over precisely these AI consent and compensation issues in the interactive sector, demonstrated how quickly a contract negotiated for one segment of the industry begins to look incomplete when applied to another. But the agreements demonstrate the principle that collective bargaining can do work that copyright cannot, by setting industry-wide floors on consent, attribution, and compensation that apply regardless of the specific upstream provenance of any given AI output.

The implication for creative workers outside the unionised entertainment sector is uncomfortable but unavoidable. The freelance illustrator, the literary translator, the independent musician, the documentary editor often have no equivalent collective body. Building one, on a national or transnational basis, becomes infrastructure rather than ideology. The European Federation of Journalists, the European Writers' Council, the International Federation of Translators, and the Concerts Promoters Association are all operating in this space, as are emerging co-operative models among illustrators in continental Europe. Any serious policy response to the structural compression of creative labour markets needs to take seriously the question of how to fund and support these bodies.

Sovereign wealth approaches and creative commons funds

A more radical proposal, occasionally floated in policy circles and yet to find a serious political champion, is the sovereign wealth approach. The argument runs that the corpus of human cultural output ingested by foundation models is a non-rival public resource analogous to a national fishery or a hydrocarbon basin. Where states extract rents from companies exploiting natural resources, the rents fund either current public expenditure or, in the Norwegian case, an intergenerational sovereign wealth fund. By analogy, a national or supranational creative commons fund, capitalised by an ingestion-based levy on commercial AI training and operation, could be invested to provide perpetual support for cultural production.

The sovereign wealth analogy is imperfect. Cultural output is not extracted from a finite reservoir; it is generated, continuously, by living people whose labour the fund is meant to compensate. But the analogy is useful precisely because it forces a recognition that the value flowing into AI labs from human cultural output is, in macroeconomic terms, an unpriced externality of historic scale. The OECD's 2025 report on intellectual property and AI training data raised, without endorsing, the question of whether the absence of pricing on this externality represents a market failure that justifies non-market correction. That is exactly the conceptual frame a sovereign wealth approach would adopt.

The global south is not a footnote

Any honest reckoning with the policy space has to confront the dimension that the IPS News analysis put squarely on the table: the income losses from generative AI are not falling evenly across geography. They are falling disproportionately on freelance and self-employed creators in the global south. UNESCO's data, repeated in the Re|Shaping Policies for Creativity report, is sobering. In developed economies, 67 per cent of people possess essential digital skills; in developing economies the figure is 28 per cent. Cultural and creative leadership in developed countries has reached 64 per cent women in some institutional categories; in developing nations it is 30 per cent. Public funding for culture sits below 0.6 per cent of global GDP and is projected to decline. Only 61 per cent of countries surveyed have intellectual property frameworks UNESCO considers adequate.

These structural baselines were already producing inequality. Generative AI compounds them. Viviana Rangel, a Colombian independent expert quoted in the IPS News analysis, framed the problem in a sentence: the region does not produce this kind of technology; it consumes it. The economic flow runs in one direction. Cultural workers in Lagos, Lima, Manila, and Karachi see their commissions evaporate as European and North American clients route work through models trained on a corpus from which their own contributions are statistically marginal. The royalties and rents from those models, when they exist at all, flow to collective management organisations and rights holders concentrated in the North Atlantic.

This dimension has implications for every instrument discussed above. A European AI cultural levy, however well designed, will tend to recapture funds from European AI providers and recycle them through European collective management organisations to predominantly European creators. That is not necessarily wrong, but it is not a global solution. The CISAC study's projection of 22 billion euros in cumulative losses to music and audiovisual creators globally over five years is a number that needs distributional analysis. Where do the losses fall? Where do the few gains fall? UNESCO's framing of the problem as a global development issue, rather than a North Atlantic intellectual property dispute, opens space for instruments that the copyright debate alone would not generate.

The most credible candidates, at this point, are international transfers built into the supranational architecture of AI governance. A share of any revenues raised through training data taxes, statutory remuneration rights, or AI cultural levies should be directed, by treaty or legislative carve-out, to a global fund supporting creators in the regions where the harm falls hardest. This is not charity. It is restitution for an extraction whose proceeds are presently retained by a small group of companies whose corpora include cultural output from every continent. UNESCO, by virtue of its mandate over cultural diversity and the global character of its 120-country reporting, is the obvious institutional vehicle, although the World Intellectual Property Organisation and the United Nations Conference on Trade and Development have credible roles to play.

The harder version of the global south argument concerns sovereignty. If a Senegalese government wants to protect its translators, illustrators, and musicians from market compression caused by foundation models trained largely outside its borders, what tools does it have? The honest answer is: not many, in the short term. National AI levies on a small market produce modest revenue. National copyright reform reaches AI labs only weakly. National public commissioning and basic income programmes are constrained by fiscal capacity. This is one reason why the architecture of any serious policy response has to be partly supranational. It is also why policy frameworks that treat the global south as an afterthought, or that solve the problem of the European illustrator while leaving the Lagos illustrator untouched, will be morally and politically unstable.

Who bears responsibility?

The question of responsibility is the one most likely to be flattened by political slogans, so it is worth taking slowly. There are at least five candidates for the moral and economic ledger, and a serious policy framework needs to assign weight to each rather than collapsing them into a single villain.

The AI labs themselves are the most obvious candidate. They built the systems whose outputs are compressing creative labour markets. They trained the models on corpora they did not pay for, in most cases, and they continue to extract economic rent from those corpora at scale. The defence offered by lab leadership tends to combine the argument that training on publicly available content is fair use with the argument that the productivity gains from foundation models will, over time, raise everyone's incomes including creators'. Both arguments are contestable. The fair use claim is being litigated across multiple jurisdictions. The productivity-spillover claim has, so far, generated almost no observable benefit for the working creators whose markets are contracting fastest. Responsibility, on any plausible reading, sits substantially with the labs, and the policy instruments above should be priced accordingly.

The platforms that distribute creative work are a second locus. Streaming services that ingest AI-generated music into the same recommendation streams as human-made music; stock image platforms that have become, in some categories, predominantly synthetic; commissioning marketplaces that allow buyers to specify AI-generated drafts as deliverables. Each of these platforms makes choices about how to label, reward, and surface human versus synthetic output. UNESCO's report observes that opaque algorithms and platform consolidation are themselves part of the structural undercutting. Procurement-style transparency requirements, content provenance standards, and labelling rules are the relevant instruments here, and platforms are properly the subjects of them.

Governments are the third candidate. They license the regulatory environment within which labs and platforms operate, and they hold the fiscal and statutory authority to introduce levies, statutory remuneration rights, public commissioning rules, and basic income schemes. They also have the slowest reflexes. The EU AI Act, the UK text-and-data-mining consultation, the patchwork of state-level AI laws in the United States, and the regulatory regimes emerging across Asia and Latin America operate on time horizons measured in years; market compression is occurring in quarters. Responsibility for that gap falls on legislatures and on public agencies that have not yet pivoted from a copyright-only frame to a market-structure frame.

The fourth candidate is the end user: the corporate or individual buyer who chooses an AI-generated cover, a synthetic voice-over, or an automated translation over a human alternative. Moral responsibility here is real but limited. Buyers respond to prices, and prices are an artefact of upstream institutional architecture; no buyer can plausibly be expected to internalise an externality the policy regime has not bothered to price. End-user weight matters most in the public sector, in editorial institutions whose readers care about provenance, and in industries where reputation rewards transparency. Disclosure rules, labelling standards, and provenance technologies make this responsibility legible and therefore actionable.

The fifth candidate, the most diffuse and the least talked about, is the public itself, conceived as the political constituency that decides whether to treat creative labour as economically valuable enough to defend. The Irish basic income scheme exists because Irish politics decided it should. The WGA and SAG-AFTRA agreements exist because audiences, in the end, did not want to consume an industry whose writers and performers were being squeezed past tolerance. The slow shift in European policy thinking towards an AI cultural levy exists because European publics and their elected representatives have, for now, not lost their attachment to the idea that cultural work is a public good worth supporting through institutional design. That political attachment is not automatic. It can erode. Where it erodes, the labs and platforms set the frame.

What a serious settlement would look like

A serious policy settlement, on the analysis above, is not a single instrument but a stack. Copyright reform sits at the bottom of the stack, addressing the upstream ingestion question that copyright is institutionally suited to handle. Statutory remuneration rights and AI cultural levies sit above it, addressing the substitution-side compression that copyright cannot reach. Public commissioning rules and procurement preferences sit above those, deploying the state's purchasing power to maintain a demand floor for human creative work. Universal creative income schemes, on the Irish model, sit above those, decoupling baseline livelihood from market success. Collective bargaining and trade-association infrastructure sits across the stack, providing the institutional capacity for creators to negotiate consent, attribution, and compensation in real-time as the technology evolves. International transfers, capitalised by the levies and routed through multilateral cultural institutions, sit on top, addressing the global south dimension that no national policy can solve alone.

No single country has the full stack today. Ireland has a basic income scheme. France and the European Union are debating a levy. The United Kingdom has a collective licensing prototype. Spain has labelling rules. Germany has a private copying levy heritage that could be retrofitted. The United States has the WGA and SAG-AFTRA agreements, the GSA procurement clause, and California's vendor certification regime. India has labelling thresholds. UNESCO has 8,100 catalogued policy measures across 120 countries. The pieces exist; what is missing is the integration.

This is, in some sense, the unromantic conclusion. The problem that generative AI has created in the creative economy is not, primarily, a problem that demands a new philosophical framework, although philosophical frameworks help. It is a problem that demands the assembly of a known set of instruments into a coherent stack, with serious institutional design and credible enforcement, and with explicit redistribution towards the creators and regions where the harm is concentrated. The political difficulty of that assembly is high. The intellectual difficulty is lower than the public debate sometimes implies.

The alternative, if no such stack is built, is reasonably easy to describe. The compression continues. The freelance creative class, in the global north and more sharply in the global south, contracts. The cultural production that survives concentrates among those with independent means, institutional employment, or audiences large enough to bypass the compression. The texture of cultural output narrows in ways that are hard to see in real time but legible in retrospect, in the same way the disappearance of regional newspapers became legible only after the fact. The labs and platforms continue to capture the rents from a corpus they did not build. Lodovico Folin-Calabi, the UNESCO director who told the press at the report's launch that the world must critically examine how these technologies are deployed and whose voices are represented, may turn out to have been describing not a turning point but a wake.

Whether the settlement gets built is, finally, a political question rather than a technical one. The technical question has answers. The political question, whether public, labs, platforms, and governments collectively decide that the structural losses already under way are worth correcting, has only the answers a generation chooses to give. The 24 per cent number is a forecast. It is also a decision, not yet made.

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References & Sources

1. UNESCO. Re|Shaping Policies for Creativity: Making Creativity Count. Fourth global monitoring report. Launched 18 February 2026. https://www.unesco.org/en/articles/creators-face-projected-global-revenue-losses-24-2028-new-unesco-report-shows
2. UNESCO Diversity of Cultural Expressions. “Making creativity matter: UNESCO's latest Re|Shaping Policies for Creativity report calls for bold policy action.” February 2026. https://www.unesco.org/creativity/en/articles/making-creativity-matter-unescos-latest-reshaping-policies-creativity-report-calls-bold-policy
3. United Nations News. “Artists face steep income decline due to AI, UNESCO finds.” 18 February 2026. https://news.un.org/en/story/2026/02/1166989
4. Inter Press Service. “Generative AI Could Deepen Inequality, Revenue Losses in Creative Industries.” February 2026. https://www.ipsnews.net/2026/02/generative-ai-could-deepen-inequality-revenue-losses-in-creative-industries/
5. CISAC and PMP Strategy. Global economic study shows human creators' future at risk from generative AI. December 2024. https://www.cisac.org/Newsroom/news-releases/global-economic-study-shows-human-creators-future-risk-generative-ai
6. AI Business / IT Pro. “Mistral CEO Arthur Mensch calls for AI cultural levy in Europe.” 2025. https://www.itpro.com/technology/artificial-intelligence/mistral-ceo-calls-for-ai-cultural-levy
7. Copyright Licensing Agency. “CLA announces development of Generative AI Training Licence.” 2025. https://cla.co.uk/development-of-cla-generative-ai-licence/
8. United States Copyright Office. Copyright and Artificial Intelligence, Part 3: Generative AI Training (pre-publication report). 2025. https://www.copyright.gov/ai/Copyright-and-Artificial-Intelligence-Part-3-Generative-AI-Training-Report-Pre-Publication-Version.pdf
9. OECD. Intellectual property issues in artificial intelligence trained on scraped data. 2025. https://www.oecd.org/en/publications/intellectual-property-issues-in-artificial-intelligence-trained-on-scraped-data_d5241a23-en.html
10. Geiger, Christophe et al. “Generative AI, Digital Constitutionalism and Copyright: Towards a Statutory Remuneration Right grounded in Fundamental Rights.” Kluwer Copyright Blog and International Review of Intellectual Property and Competition Law (Springer Nature). 2025. https://link.springer.com/article/10.1007/s40319-025-01569-6
11. European Parliament. Generative AI and Copyright: Training, Creation, Regulation. Study commissioned by the JURI Committee. 2025. https://www.europarl.europa.eu/RegData/etudes/STUD/2025/774095/IUST_STU(2025)774095_EN.pdf
12. Government of Ireland, Department of Culture, Communications and Sport. “Basic Income for the Arts Scheme 2026-2029 Guidelines for Application.” April 2026. https://www.gov.ie/en/department-of-culture-communications-and-sport/publications/basic-income-for-the-arts-scheme-2026-2029-guidelines-for-application/
13. CNN Style. “Ireland is paying artists a basic income in a pioneering scheme.” 12 February 2026. https://www.cnn.com/2026/02/12/style/ireland-artists-basic-income-intl-scli
14. Perkins Coie. “Generative AI in Movies and TV: How the 2023 SAG-AFTRA and WGA Contracts Address Generative AI.” 2023. https://perkinscoie.com/insights/blog/generative-ai-movies-and-tv-how-2023-sag-aftra-and-wga-contracts-address-generative
15. Center for Democracy and Technology. “New WGA Labor Agreement Gives Hollywood Writers Important Protections in the Era of AI.” 2023. https://cdt.org/insights/new-wga-labor-agreement-gives-hollywood-writers-important-protections-in-the-era-of-ai/
16. Bird & Bird. “Like Company v Google: CJEU Holds First-Ever Hearing on Generative AI and Copyright on 10 March 2026.” 2026. https://www.twobirds.com/en/insights/2026/like-company-v-google-cjeu-holds-first-ever-hearing-on-generative-ai-and-copyright
17. Gibson Dunn. “GSA AI Procurement Rules Would Introduce New Disclosure and Use-Rights Requirements for Federal Contractors.” 2025. https://www.gibsondunn.com/gsa-ai-procurement-rules-would-introduce-new-disclosure-and-use-rights-requirements-for-federal-contractors/
18. Ropes & Gray. “Newsom Signs Executive Order Establishing AI Vendor Certification and Procurement Framework.” April 2026. https://www.ropesgray.com/en/insights/alerts/2026/04/newsom-signs-executive-order-establishing-ai-vendor-certification-and-procurement-framework
19. Euronews. “Spain could fine AI companies up to €35 million for mislabelling content.” 12 March 2025. https://www.euronews.com/next/2025/03/12/spain-could-fine-ai-companies-up-to-35-million-in-fines-for-mislabelling-content

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Tim Green UK-based Systems Theorist & Independent Technology Writer

Tim explores the intersections of artificial intelligence, decentralised cognition, and posthuman ethics. His work, published at smarterarticles.co.uk, challenges dominant narratives of technological progress while proposing interdisciplinary frameworks for collective intelligence and digital stewardship.

His writing has been featured on Ground News and shared by independent researchers across both academic and technological communities.

ORCID: 0009-0002-0156-9795 Email: tim@smarterarticles.co.uk

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The knock comes on a Tuesday, late afternoon, when the rice is still on the hob and the youngest is doing homework at the kitchen table. A caseworker in a thin coat introduces herself, asks if she can come in, and explains that the city has received a report and is required to follow up. The mother, who has lived in the same flat for nine years and has never had a child welfare investigation in her life, asks who made the report. The caseworker hesitates. It is not exactly a report, she says. It is a flag.

This is the moment, repeated thousands of times a year across American cities, when a family discovers that they have been the subject of attention they did not know was possible. The flag did not come from a neighbour or a teacher or a paediatrician. It came from a model. A risk-scoring system, fed on years of administrative data, generated a number that crossed a threshold inside a software dashboard at the local child protective services office. A screener saw the number. A supervisor signed off. A caseworker was dispatched. Somewhere along that chain, a human being had to make a final decision, but the decision was anchored, framed, and quietly shaped by an output that nobody in the home would ever see.

The mother in the flat has no right to see the score. She has no right to know which features pushed her family above the threshold. She has no right to challenge any one of those features in front of a neutral reviewer. She has no right, in any meaningful sense, to know that the algorithm exists.

That is where American child welfare sits in the spring of 2026: an expanding lattice of predictive systems, deployed inside agencies whose decisions can place a family under state surveillance and, in the worst cases, separate parents from their children, operating almost entirely outside the procedural rights that any other consequential decision in modern life would attract. A family flagged by a credit-scoring algorithm has more statutory recourse than a family flagged by a child welfare risk model. A driver flagged by a parking enforcement camera has more transparency. A tenant flagged by an algorithmic landlord screen has more legal scaffolding to push back. The state has built one of the most invasive deployments of pattern-matching in American public administration, and it has done so on top of the thinnest possible layer of due process.

The Markup Investigation and What It Found

In 2025, The Markup published an investigation into the Administration for Children's Services in New York City, the agency that handles child abuse and neglect reports for roughly 1.6 million children. The investigation, drawing on internal documents and interviews with agency staff, established that ACS had been using an algorithmic risk-scoring tool to help decide which families warranted heightened scrutiny, surveillance and investigation following a hotline call. The tool, which the agency had introduced years earlier with limited public discussion, generated a score for every family entering the system, and that score informed which cases were elevated for what staff called “high-priority” review.

The Markup's reporters, working with academic researchers, found that the system disproportionately flagged Black and low-income families at rates higher than would be expected from the underlying base rates of confirmed maltreatment in those populations. The disparity was not fully explained by the data the agency claimed to be using. There were other variables, less obvious ones, that appeared to be doing meaningful work inside the model. Postcodes. Prior contact with public assistance programmes. Density of services in a neighbourhood. Each was, on its face, a non-racial input. Each, in practice, served as a proxy for race and class, because race and class in New York are written into the geography and the administrative trail of poverty.

The agency, when contacted, defended the tool. It pointed out that the score was advisory, that humans made the final calls, that the system had been validated internally. The agency declined to release the model's full feature set. It declined to release the weights. It declined to release the technical documentation that would have allowed independent researchers to reproduce the disparity findings or to test the model on counterfactual data. Families who had been flagged by the tool, and who had then had caseworkers in their homes, had no idea that an algorithm had been involved in the decision.

The Markup investigation matters not because it was the first time anyone had documented this pattern. It matters because it landed in the largest city in the United States, in the agency that handles the largest child welfare caseload in the country, and because it confirmed that what had previously been a research finding from smaller jurisdictions was now a continental-scale phenomenon. Child welfare is being run, in part, by black-box prediction.

The Allegheny Lineage

The patient zero of the modern child welfare risk-scoring movement is the Allegheny Family Screening Tool, deployed in Allegheny County, Pennsylvania, beginning in 2016. The tool was developed by a research consortium, validated using historical case data, and integrated into the county's call-screening process. When a hotline operator received a report, the tool produced a score that estimated the likelihood that a child in the household would be removed within two years. Higher scores triggered closer review.

The Allegheny tool was, in many ways, the public face of a movement that promised to bring rigour and consistency to an area of public administration long accused of being inconsistent and biased. Its developers were not naive technocrats. They were academics with serious credentials in social welfare and statistics, and they argued, plausibly, that human screeners themselves were biased, and that an algorithm trained on the same data could at least be audited. The tool was not deployed in secret. There were public meetings, advisory committees, journalistic profiles. For a brief moment in the late 2010s, Allegheny was held up as the responsible model.

What followed was a decade of audits that complicated that picture. Independent researchers, including teams who built fairness audit frameworks specifically for child welfare contexts, found that the tool's predictions correlated with socioeconomic status in ways that were not adequately disclosed in the public materials. They found that the tool's accuracy varied by demographic group. They found that the underlying training data, which was based on historical screening and removal decisions, encoded the biases of the human system the tool was supposed to improve. If the historical data showed that Black families in Allegheny had been more likely to have their children removed for any given maltreatment report, then a model trained on that data would learn to flag Black families more aggressively, and would do so even if every explicit racial variable was stripped from the inputs.

The Allegheny defenders responded that the tool reduced overall disparity compared with unaided human screening, and there is research that supports parts of that claim. The Allegheny critics responded that “less biased than the worst-case human” is not a high enough bar to justify deployment, particularly when the tool's mechanics remained opaque to the families it scored. By the early 2020s, this argument had calcified into a sort of trench warfare in the academic literature. The county kept using the tool. Other jurisdictions copied it. New York City's ACS was one of those jurisdictions, and the tool documented by The Markup is, in effect, a descendant of the Allegheny lineage, retrained on different data and tuned to different operational thresholds.

What the April 2026 Audits Showed

On 21 April 2026, two papers appeared on arXiv that, between them, gave the most rigorous picture yet of what is actually happening inside these systems. The first was a fairness audit of institutional risk models in welfare and safeguarding contexts. The second was an analysis of algorithmic fairness in case-note-augmented prediction systems, the newer generation of tools that pull free-text narrative from caseworker notes into the feature pipeline.

The findings, taken together, are damning in a precise and technical way. Models deployed in high-stakes welfare and safeguarding contexts routinely encode socioeconomic and racial proxies even when those variables are nominally excluded from the input set. The mechanisms are not mysterious. They are documented. Postcodes function as racial proxies in segregated cities. Prior interactions with means-tested benefits encode income and, indirectly, race. Neighbourhood-level deprivation indices, which were originally designed by social scientists to identify communities in need of investment, become, when fed into a risk model, indicators that an individual family is more dangerous to its own children. Each input, considered alone, has a defensible policy rationale. Stacked, weighted and combined inside a model that was optimised to predict historical removals, they produce a system that reproduces the geography of state intervention with eerie fidelity.

The case-note paper went further. Once a model starts ingesting free-text notes from caseworkers, the proxy problem deepens, because language itself is socially stratified. A caseworker note that describes a home as “chaotic” or a parent as “uncooperative” carries weight inside an embedding model. Whether those labels were accurate, fair, or applied consistently across demographic groups is a question the model cannot answer and the deployment process rarely interrogates. Audits showed that case-note-augmented models could amplify existing disparities, because the historical record of how caseworkers described different families itself encoded assumptions about whose homes were suspect.

Both papers stopped short of saying that current child welfare risk models cannot be made fair. Both papers said, in different ways, that current child welfare risk models are not currently fair, that their unfairness is structural rather than incidental, and that the standard mitigations on offer in the technical literature, group-balanced thresholds, adversarial debiasing, fairness constraints during training, are insufficient to address proxy encoding at the depth it currently operates. To put it bluntly: the tools the field has built to make these systems fair are themselves not powerful enough to overcome the data the systems are trained on.

The Berkeley Notice Problem

In January 2026, researchers at the University of California, Berkeley published an analysis of a different but related question. Not whether these systems are fair in a statistical sense, but whether the people they scored had any meaningful idea that scoring was happening. The Berkeley analysis catalogued the deployment of algorithmic decision systems across a growing range of life-altering institutional contexts, including child welfare assessments, public benefit eligibility, criminal justice risk assessment, healthcare allocation, and tenant screening. It found that, in the overwhelming majority of cases, the affected individual had no notice that an algorithm was involved, no access to an explanation, and no formal route of appeal that engaged with the algorithmic component of the decision specifically.

This is the harder problem, and in some ways the more politically tractable one. Statistical fairness is a moving target. Technically, you can argue forever about whether a particular calibration metric or error-rate parity standard is the right one. Notice and explanation are simpler. Either the family knows that a system was used or they do not. Either there is a document explaining the inputs or there is not. Either there is a procedure for contesting the score or there is no such procedure.

The Berkeley researchers' finding, applied to child welfare, is sobering. A family flagged by a risk-scoring tool in New York or Pittsburgh or Los Angeles has, in practice, no way to know that they were flagged by a tool. The caseworker on the doorstep is not required to tell them. The investigation paperwork does not disclose it. The records request, if they know to file one, may or may not produce the score. If it does, it almost certainly will not produce the underlying feature values, the model card, or any documentation that would allow them to understand what was being weighed and how.

The information asymmetry is total, and it sits on top of an existing power asymmetry that is itself substantial. Families in the child welfare system are disproportionately poor, disproportionately non-white, and disproportionately under other forms of state observation already, including housing assistance, food assistance, public schools, and Medicaid. The institutional knowledge of how to navigate any of these systems is unevenly distributed. Add an opaque algorithmic layer on top of all of that, and the result is a population of citizens making decisions, accepting investigations, and signing service plans without knowing one of the most important inputs into the state's interest in them.

Why No Rights Yet

The legal infrastructure that might check any of this exists, in patches, in places. None of it is robust enough to do the job.

At the federal level, the closest analogue to a comprehensive algorithmic accountability statute is the patchwork of civil rights law, which prohibits disparate-impact discrimination in some federally funded programmes but has never been successfully wielded against a child welfare risk-scoring tool in the way it has against, say, mortgage-lending models. The procedural due process clause of the Fourteenth Amendment offers some protection in cases where the state seeks to terminate parental rights, but the protections kick in late in the process, well after the algorithmic flag has done its work to set events in motion. Pre-investigation flags are not adjudicated. They are operational decisions, treated as administrative discretion, and discretion is precisely what courts have historically been reluctant to second-guess.

At the state level, a handful of legislatures have passed bills requiring agencies to disclose when they are using automated decision systems, but most of these laws contain carve-outs for “decision-support” tools, and almost every child welfare risk model is officially classified as decision-support rather than as automated decision-making. The reasoning is that a human screener still signs off. The reality is that the screener is reading a score that has been generated by software, and the score functions as the primary signal in many of those decisions. The carve-out exists because vendors and agencies argued for it, and because legislators who wrote the bills did not want to be accused of weakening safeguarding by wrapping it in transparency requirements.

At the procurement level, the contracts that govern these tools are often classified as confidential commercial information. Vendors negotiate terms that prohibit agencies from disclosing the model's inner workings, on the theory that the model is intellectual property. Agencies, who frequently do not have in-house data science capacity to evaluate the tools, accept these terms because they want the tools and they cannot easily build them themselves. The result is a procurement architecture in which the state delegates a consequential public function to a private contractor, accepts secrecy as a condition of the contract, and then refuses to disclose what it has bought, on the grounds that the secrecy is the vendor's right.

The contrast with the European Union is instructive. Article 22 of the General Data Protection Regulation, in its original 2018 form, gave individuals a right not to be subject to decisions based solely on automated processing that produce legal or similarly significant effects. The right was always more limited in practice than in headline, because purely automated decisions are rare and most regulated decisions involve some human in the loop. But Article 22, paired with the wider GDPR architecture of subject access rights, transparency obligations, and data protection impact assessments, created a baseline that simply does not exist in the United States. The 2024 EU AI Act extended this baseline with risk-tiered obligations for high-risk systems, including those used in social welfare administration. The United States has nothing equivalent at the federal level. State-by-state, the strongest American statutes on automated decision-making are weaker than the floor European regulators consider unacceptable.

This is not because Americans are less concerned about state surveillance of families. It is because the American legislative process, on questions of child welfare, has a particular political shape. No politician wants to be the one who voted for the bill that allegedly weakened child protection. Every vendor of a risk model can frame transparency requirements as obstacles to keeping children safe. Every agency that uses one of these tools can claim that disclosure of the model's mechanics would teach abusive parents how to game the system. These framings are sometimes sincere and sometimes opportunistic, and both are politically effective. The result is a legislative landscape in which proposals to give families notice and challenge rights die in committee, while procurement contracts for new tools are renewed without serious public debate.

Voices in the Field

The academic literature on algorithmic harm in welfare contexts is, by 2026, large enough to constitute a small subfield. Virginia Eubanks, whose 2018 book on automated inequality remains foundational, argued that the deployment of predictive tools in welfare administration represents a new form of digital poorhouse, applying mass surveillance to the populations least able to resist it. Dorothy Roberts, whose work on the racial politics of family policing predates the algorithmic era, has long argued that the child welfare system is structurally biased against Black families and that data-driven tools, far from correcting that bias, formalise it and make it harder to contest. Rashida Richardson, who has written on algorithmic accountability and government use of predictive systems, has argued for procedural rights of notice, explanation and contestation as a baseline condition of legitimate deployment.

On the technical side, researchers like Solon Barocas have spent years documenting the mechanisms by which proxy variables encode protected attributes, and the limits of formal fairness criteria in the face of those mechanisms. Hadi Elzayn and collaborators have published audits of welfare-adjacent algorithmic systems showing, with empirical rigour, how disparate impact persists even under well-designed mitigation strategies. None of these scholars has called for a complete ban on predictive tools in welfare contexts. Most have called for a combination of structural reforms: independent audits, transparency requirements, due process rights, and a presumption that high-stakes deployments require a much higher evidentiary bar than what is currently common practice.

The interesting feature of this body of work is how unified it is on the procedural questions, even when scholars disagree on the technical questions. Whether a particular fairness metric is the right one is contested. Whether families should have a right to know that a model was used in a decision that affected them is, within this literature, essentially uncontested. The gap between the academic consensus and the operational reality of American child welfare is wide, and it is not narrowing.

What Rights Would Look Like

The shape of a meaningful rights framework for algorithmic decisions in child welfare is, at this stage, well rehearsed in policy literature. The components are not exotic.

Notice would mean that a family receiving a child welfare contact would be told, in writing, whether an algorithmic risk-scoring tool was used in the decision to investigate, and that they would be given the name and a plain-language description of the tool. This is an extremely low bar. It would not change the outcome of any individual investigation. It would simply close an information asymmetry that currently has no defensible justification.

Access would mean that the family could obtain the score that was generated for them, the inputs that fed into the score, and the documentation describing how the model translates inputs into outputs. The technical documentation already exists in most cases. It is generated as part of the procurement process. The barrier to disclosing it is contractual, not technical.

Contestation would mean that the family could challenge specific data points used in the score. This is where the model intersects with longstanding administrative law practice. Government records routinely contain errors. Some of those errors are typographical. Others are substantive. A family who has been flagged on the basis of a prior investigation that was later closed as unfounded should be able to point at that investigation and ask whether it was correctly weighted in the model. A family flagged on the basis of a postcode association should be able to ask whether that association is what is doing the work and, if so, whether the weight is justified.

Human review with authority would mean that the human in the loop is not just a person who reads the score and signs off, but a person with the institutional standing to overturn the score, the time to actually examine the inputs, and a documented record of the reasoning behind their decision. This is the most demanding component, because it requires resourcing and training that most agencies have not invested in. It is also the most consequential, because it transforms the human-in-the-loop from a procedural fig leaf into a real check.

Independent auditing would mean that agencies cannot simply self-validate their tools. They would be required to submit the tools to external technical review, including review by parties with no commercial interest in the tool's continued deployment. Audit findings would be public. Significant findings would trigger remediation requirements with deadlines.

A route of appeal would mean that there is a forum in which a family can challenge an algorithmically influenced decision and obtain meaningful relief. This is the hardest component to graft onto the existing child welfare system, because the system's procedural backbone is calibrated for a different kind of dispute. It is calibrated for fact-finding about events in a household, not for technical contestation of a model's behaviour. Building this capacity would require new staff, new training, and probably a new tier of administrative tribunal.

None of these proposals is technically novel. Each has been articulated in academic and policy literature. Each, in some form, exists in other regulatory contexts. What is missing is not the design. What is missing is the political coalition to build them in.

Why the Politics Are Stuck

The reasons no such coalition has consolidated are visible in the structure of the issue. Child protection, as a political project, runs on the premise that the state's job is to err on the side of intervention. The institutional culture of the agencies, the framing of legislative debates, and the media treatment of failures all push in one direction. When a child is harmed in a family that the system did not investigate, there are inquiries, commissions and resignations. When a family is harmed by an unjustified investigation, the story tends not to make the front page, and the family tends not to have a press office.

This asymmetry shapes how risk-scoring tools are introduced and how they are defended. The pitch to administrators is that the tool will reduce the rate of false negatives, the cases where the system missed a child who needed protection. The pitch to legislators is similar. The cost on the other side, the rate of false positives, the families subjected to investigation they did not need, is rarely treated as a comparable harm in the political conversation, even though it is a quantifiable and substantial cost in the lives of those families. The current generation of risk-scoring tools is calibrated according to thresholds chosen by agency leadership, and those thresholds are typically set conservatively in the direction of investigating more rather than fewer households.

The vendors of these tools have learned to operate within this politics. They market on the prevention of catastrophic outcomes. They underplay the operational disparities. They negotiate procurement contracts that limit disclosure. They cultivate relationships with academic researchers who can supply the legitimating veneer of validation studies. None of this is corrupt in any obvious sense. It is the normal behaviour of any commercial actor selling into a politically sensitive market with high stakes and asymmetric information. But the cumulative effect is an industry that is poorly disciplined by external oversight, because the external oversight does not have the tools to discipline it.

Affected families, meanwhile, are nearly impossible to organise. They are already under state scrutiny. They are reluctant to draw additional attention to themselves. They often do not know that other families have had similar experiences, because the information that would allow them to find each other does not flow. Civil society organisations have done significant work in this area, but they have done it at a scale that is dwarfed by the operational scale of the agencies and vendors they are trying to hold accountable.

What to Watch

The most likely vector of change in the near term is litigation. Several civil rights organisations have been preparing cases that target specific algorithmic deployments in welfare contexts, looking to establish precedent under existing civil rights and due process doctrine. The legal theory would not require a new statute. It would require a court to recognise that a family has a constitutionally cognisable interest in not being subjected to investigation on the basis of a process that they cannot contest. Whether such a case will succeed is uncertain. The doctrine is unfriendly. The factual records are hard to build. But the architecture of the litigation is plausible enough that several organisations are betting on it.

A second vector is local legislation, particularly in cities and states where the political balance is more amenable to civil liberties framings. New York, in the wake of The Markup investigation, has seen renewed legislative interest in algorithmic accountability for city agencies. Whether ACS specifically will be brought under stronger transparency rules remains to be seen. The vendors have lobbyists. The agency has institutional inertia. But the political weather, in 2026, is more favourable to disclosure than it was in 2020, and the gap between civil society capacity and vendor capacity is starting to narrow as algorithmic accountability becomes a more established advocacy field.

A third vector, and the one most aligned with the academic literature, is the construction of an external audit infrastructure. A non-governmental organisation, an academic consortium, or a hybrid public-private body with the technical capacity to audit child welfare risk-scoring tools and the legal standing to compel disclosure does not currently exist in the United States. Building one would require funding, talent, and a political settlement that recognises external audit as a legitimate function. There are precedents in other regulated industries: financial auditing, environmental impact assessment, clinical trial review. The case for an analogue in algorithmic public administration is, in the wake of the April 2026 audit findings, harder to dismiss than it once was.

The Family Comes Back

The mother in the flat does not see any of this. She sees a caseworker on a Tuesday afternoon. She answers questions she did not expect to answer. She watches her children watched by a stranger. She signs paperwork. The investigation, in her case, is closed without findings six weeks later. She is not removed from the system; she is now in it, in the database, as a household with a prior contact, a feature that may itself be ingested by the next iteration of the model the next time her name comes up.

She is told none of this. She is not told that an algorithm was involved, that her postcode contributed to the flag, that the model's developers have already been audited by independent researchers and found wanting. She is not told that the city paid a vendor several million dollars for the tool, or that the vendor's contract prohibits disclosure of the model's inner workings. She is not told that, in another country with a different legal regime, she would have had a statutory right to ask for and receive an explanation of the decision that put a stranger in her kitchen.

If American child welfare is going to have any meaningful answer to the question of what happened to her, the answer will not come from the agencies that deployed the tools or from the vendors that built them. It will come from courts willing to take procedural due process seriously when it is dressed in code, from legislators willing to pass disclosure requirements that survive vendor lobbying, and from a civil society infrastructure that does not yet exist at the scale the problem demands. The April 2026 audits, and the Berkeley analysis from earlier in the year, and the Markup investigation that preceded both, are not a complete map of the problem. They are a sufficient one. The technology is here. The harms are documented. The scaffolding of rights is a decade behind.

The next time the knock comes, the family on the other side of the door deserves, at minimum, a piece of paper that tells them what they are dealing with. That is not a radical demand. It is the floor.

References

1. The Markup. (2025). Investigation into the Administration for Children's Services algorithmic risk-scoring tool, New York City. The Markup.
2. Eubanks, V. (2018). Automating Inequality: How High-Tech Tools Profile, Police, and Punish the Poor. St. Martin's Press.
3. Roberts, D. E. (2022). Torn Apart: How the Child Welfare System Destroys Black Families and How Abolition Can Build a Safer World. Basic Books.
4. Chouldechova, A., Putnam-Hornstein, E., Benavides-Prado, D., Fialko, O., & Vaithianathan, R. (2018). A case study of algorithm-assisted decision making in child maltreatment hotline screening decisions. Proceedings of Machine Learning Research, 81, 134-148.
5. Vaithianathan, R., Maloney, T., Putnam-Hornstein, E., & Jiang, N. (2017). Children in the public benefit system at risk of maltreatment: Identification via predictive modeling. American Journal of Preventive Medicine, 45(3), 354-359.
6. Allegheny County Department of Human Services. (2019). Allegheny Family Screening Tool: Methodology, Version 2. Allegheny County, Pennsylvania.
7. Richardson, R., Schultz, J., & Crawford, K. (2019). Dirty data, bad predictions: How civil rights violations impact police data, predictive policing systems, and justice. New York University Law Review Online, 94, 192-233.
8. Barocas, S., & Selbst, A. D. (2016). Big data's disparate impact. California Law Review, 104, 671-732.
9. Anonymous authors. (2026, 21 April). Fairness audits of institutional risk models in welfare and safeguarding contexts. arXiv preprint.
10. Anonymous authors. (2026, 21 April). Algorithmic fairness in case-note-augmented prediction systems. arXiv preprint.
11. UC Berkeley research team. (2026, January). Notice, explanation, and appeal in life-altering algorithmic decisions: An empirical analysis. University of California, Berkeley.
12. European Parliament and Council. (2016). Regulation (EU) 2016/679 (General Data Protection Regulation), Article 22.
13. European Parliament and Council. (2024). Regulation (EU) 2024/1689 (Artificial Intelligence Act).
14. Elzayn, H., Black, E., Vossler, P., et al. (2023). Measuring and mitigating racial disparities in tax audits. Stanford Institute for Economic Policy Research, working paper.
15. Eubanks, V., & Mateescu, A. (2021). “We don't deserve this”: New app places homeless services under surveillance. Logic Magazine.
16. Hurley, D. (2018). Can an algorithm tell when kids are in danger? The New York Times Magazine, 2 January.
17. Brown, A., Chouldechova, A., Putnam-Hornstein, E., Tobin, A., & Vaithianathan, R. (2019). Toward algorithmic accountability in public services: A qualitative study of affected community perspectives. Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems.
18. Saxena, D., Badillo-Urquiola, K., Wisniewski, P. J., & Guha, S. (2020). A human-centered review of algorithms used within the U.S. child welfare system. Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems.
19. Ho, D. E., & Engstrom, D. F. (2020). Algorithmic accountability in the administrative state. Yale Journal on Regulation, 37, 800-854.
20. Citron, D. K., & Pasquale, F. (2014). The scored society: Due process for automated predictions. Washington Law Review, 89, 1-33.

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Tim Green UK-based Systems Theorist & Independent Technology Writer

Tim explores the intersections of artificial intelligence, decentralised cognition, and posthuman ethics. His work, published at smarterarticles.co.uk, challenges dominant narratives of technological progress while proposing interdisciplinary frameworks for collective intelligence and digital stewardship.

His writing has been featured on Ground News and shared by independent researchers across both academic and technological communities.

ORCID: 0009-0002-0156-9795 Email: tim@smarterarticles.co.uk

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