Tracking AI incidents: OECD AIM and AIAIAC Repository

Summary

This comprehensive resource examines two groundbreaking initiatives that are reshaping how we document and learn from AI failures: the OECD's AI Incidents and Hazards Monitor (AIM) and the AIAAIC Repository. Unlike theoretical risk frameworks, these platforms catalog real-world AI incidents—from algorithmic bias in hiring tools to autonomous vehicle crashes—creating an evidence base that governance leaders, researchers, and developers can use to understand where AI systems actually fail in practice. By analyzing patterns across hundreds of documented incidents, these repositories transform isolated failures into actionable insights for building more robust AI systems and policies.

The dual approach: Why two repositories matter

The OECD AIM and AIAIAC Repository complement each other in powerful ways. The OECD's platform brings institutional credibility and government backing, making it a trusted source for policy makers drafting AI regulations. It focuses on incidents that have clear governance implications and maintains rigorous documentation standards that can inform international AI policy coordination.

The AIAIAC Repository, meanwhile, casts a wider net with more agile documentation processes. It captures emerging incidents quickly and includes a broader range of AI-related controversies, from data privacy violations to algorithmic amplification of misinformation. Together, they provide both the authoritative record needed for policy-making and the comprehensive coverage required for research and risk assessment.

What patterns reveal about AI risks

These repositories don't just collect stories—they reveal systemic patterns that would be invisible when looking at individual incidents. Common themes include bias amplification in high-stakes decisions (hiring, lending, criminal justice), performance degradation when AI systems encounter data different from their training sets, and cascading failures where one AI system's errors trigger problems in connected systems.

The documentation shows that many incidents share root causes: inadequate testing on diverse populations, insufficient human oversight in critical decision points, and poor integration between AI systems and existing processes. This pattern recognition enables organizations to proactively address vulnerability classes rather than reactively fixing individual problems.

Who this resource is for

AI governance professionals developing incident response protocols and risk management frameworks for their organizations, particularly those needing real-world examples to support policy recommendations or justify governance investments.

Policy makers and regulators crafting AI legislation who need evidence-based examples of where current oversight gaps exist and what types of incidents warrant regulatory attention.

AI safety researchers and risk analysts looking for comprehensive datasets to study failure modes, identify emerging risk patterns, or validate theoretical risk models against real-world outcomes.

Chief AI Officers and technical leaders responsible for AI system deployment who want to learn from others' mistakes and benchmark their own incident response capabilities against documented cases.

Getting the most from incident data

Start by filtering incidents by sector, AI application type, or harm category most relevant to your context. Both repositories offer taxonomies that help you focus on comparable situations rather than getting overwhelmed by the full scope of documented incidents.

Pay attention to the "contributing factors" and "lessons learned" sections when available—these often contain the most actionable insights for prevention. Look for incidents where the technical failure mode matches your AI systems' capabilities, but also examine cases where similar business processes or stakeholder relationships created vulnerabilities.

Use the temporal data to understand how certain types of incidents have evolved. Early incidents in a domain often reveal fundamental design flaws, while later incidents might indicate more sophisticated failure modes or the emergence of adversarial behaviors.

Watch out for documentation gaps

While these repositories represent the most comprehensive incident tracking available, they suffer from significant reporting bias. High-profile incidents in regulated industries or involving large technology companies are well-documented, but failures in smaller organizations, emerging markets, or less visible applications are likely underrepresented.

The repositories also vary in their technical depth. Some incidents include detailed technical analyses of failure modes, while others provide only high-level descriptions of impacts. When using this data for risk assessment, be cautious about generalizing from incidents where the underlying technical details aren't fully documented.

Finally, remember that these are reactive systems—they document incidents after they occur and are discovered. They can't capture near-misses, internal failures that organizations choose not to disclose, or incidents where the AI contribution to harm isn't immediately recognized.