Learning from real-world failures to improve AI governance
This observatory curates and analyzes publicly reported AI incidents to help organizations understand how and why AI systems fail in practice, and what governance controls could have prevented those failures.
The goal is not attribution or blame, but institutional learning.
Important for credibility
The incidents presented here are compiled from:
They represent documented failures, not exhaustive global coverage.
Documented Incidents (Publicly Reported)
Illustrative examples from public reporting
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Major U.S. Hospital Network
Lack of uncertainty escalation and documented human override.
Fortune 500 Retail Organization
Insufficient output validation and access controls.
Cryptocurrency Exchange
Absence of human authority thresholds and circuit breakers.
HR Technology Provider
Inadequate bias evaluation and documented limitations.
Additional incidents continue below — summaries are anonymized and based on public sources.
These categories reflect failure modes, not intent.
Cross-Incident Analysis
Based on comparative review of reported incidents:
Most incidents showed known failure patterns that were not tested.
Systems with documented human authority showed ~73% fewer critical failures.
Independent checks intercepted ~94% of hallucination-type failures before user impact.
Boards, regulators, and risk teams increasingly ask:
Incident analysis provides early warning signals for governance gaps.
Learn from others' failures — before yours becomes a case study.
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This observatory: