The New York Times published a fabricated quote from a political leader, generated by AI, attributed to a specific speech on a specific date. Neither happened.
“The reporter should have checked the accuracy of what the A.I. tool returned.” — New York Times correction
“The tool provided links to a video of a speech as well as purported transcribed quotes from that speech. The remark we initially published was, in fact, an A.I.-generated summary incorrectly rendered as a transcript.” — NYT spokesperson
The New York Times’ Canada bureau chief used a generative AI tool to locate remarks by Conservative leader Pierre Poilievre. The AI returned a fabricated quote, attributed to a speech in March that did not contain those words, and the reporter published it as a direct quotation. The fabrication was caught not by editors or fact-checkers but by a reader on Bluesky who could not find the quote in any public record. The correction took over two weeks to appear. The Times’ own AI policy requires that all AI-assisted content begin with vetted factual information and be reviewed by editors. The incident follows other AI fabrication cases at the Times: a freelance book critic who plagiarized via AI, and a summer reading list populated with made-up book titles. The Walrus investigation noted that these are only the failures conspicuous enough to catch, raising the question of how many routine AI fabrications go undetected.
quote was corrected
(not editors)
incident at the Times
AI tools that fabricate quotes attributed to real people on specific dates are not making errors. They are generating false evidence. The Sullivan & Cromwell hallucination put a fake case citation in a court filing. This incident put a fake political quote in the most widely read newspaper in the world. Both failures share a root cause: the humans using the AI trusted its output without independent verification. AVAAS certification tests whether AI systems fabricate verifiable claims, including quotes, citations, credentials, dates, and institutional affiliations. A model that generates a fabricated direct quote attributed to a named individual does not pass certification.
This entry is one of 37 documented cases in the AVAAS evidence ledger, a public record of AI and automated-system failures with a verified source on every entry.
Every case here reached a person.
AVAAS certifies how AI systems behave at the decision point, with documented third-party evidence of conformity to a published standard.
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