Our Mission
AI fails in the real world.
We document it.
AI Failure Intelligence is a structured research dataset built for the people working to make AI systems safer — researchers, red-teamers, auditors, and enterprise risk teams.
4,600+
Cases Documented
Growing daily
24
Industry Domains
Healthcare to Finance
12
Failure Types
Standardized taxonomy
8
Risk Patterns
Cross-domain patterns
How We Work
Analyst Methodology
01
Source Discovery
We monitor 40+ primary sources: regulatory filings (FDA, FTC, NHTSA), academic databases, investigative outlets, and practitioner channels.
02
Verification
No case is added without at least two independent sources. Disputed facts are clearly labeled.
03
Taxonomy Application
Each case is structured across 18 fields: failure type, risk pattern, severity scores, contributing factors, and timeline.
04
Intelligence Layer
Pro cases get deep enrichment: regulatory correspondence, expert commentary, and remediation playbooks.
05
Ongoing Maintenance
Cases evolve. We update existing cases when new information emerges and track long-running incidents.
Who Uses This
Built for These Use Cases
🔬
Academic Research
Empirical datasets for studying AI failure modes and deployment risk.
🛡
Red Teaming
Real failure patterns to stress-test AI systems before deployment.
🏛
Policy & Governance
Evidence base for AI regulation and standards discussions.
⚙️
Enterprise Risk
Understand what can go wrong before deploying AI at scale.
📚
AI Safety Education
Case studies for responsible AI courses and workshops.
📰
Investigative Journalism
Structured background on AI incidents for reporters.
Start with 10 Free Cases
No credit card required. Browse the dataset, see the structure, upgrade when ready.