AIAAIC Repository

Summary

The AIAAIC Repository stands as the internet's most comprehensive public database of AI-related failures, controversies, and harmful outcomes. More than just a collection of incidents, this OECD-backed resource provides structured data, analytical tools, and standardized metrics that transform scattered news stories and academic papers into actionable intelligence for AI governance. Whether you're tracking algorithmic bias in hiring systems, documenting facial recognition failures, or analyzing patterns in AI safety incidents, this repository offers both the raw data and the analytical framework to turn incidents into insights.

What makes this different

Unlike scattered news reports or academic papers that document AI problems in isolation, the AIAIAC Repository creates a structured, searchable ecosystem of incident data. Each entry isn't just a description—it's tagged with standardized categories covering everything from the type of AI system involved to the nature of harm caused. The repository includes incidents spanning facial recognition errors that led to wrongful arrests, algorithmic bias in healthcare that affected patient outcomes, and autonomous vehicle failures that resulted in accidents.

What sets this apart is its focus on accountability metrics. Rather than simply cataloging problems, the repository tracks responses: Did companies acknowledge the issue? Were fixes implemented? What regulatory action followed? This creates a longitudinal view of how the AI ecosystem handles failures and learns from mistakes.

The data landscape you're entering

The repository organizes incidents across multiple dimensions that matter for governance work. You'll find cases categorized by AI application area (healthcare, criminal justice, transportation), type of harm (discrimination, safety failures, privacy violations), affected populations, and geographic scope. Each incident includes timeline data, allowing you to track how problems evolved and were addressed over time.

The database doesn't just focus on dramatic failures—it includes subtler but systemic issues like gradual algorithmic drift in content moderation systems or documented patterns of bias in seemingly neutral automation tools. This comprehensive approach means you're getting visibility into both headline-grabbing incidents and the quieter patterns that often reveal more about systemic issues.

Who this resource is for

AI governance professionals and policy researchers who need evidence-based examples to support regulatory frameworks, risk assessments, or policy recommendations. The structured data format makes it easy to generate reports showing incident patterns across sectors or jurisdictions.

Corporate AI ethics and risk management teams building internal incident response processes or conducting AI impact assessments. The repository provides real-world examples of what can go wrong and how organizations have (or haven't) responded effectively.

Academic researchers and journalists investigating AI accountability, algorithmic transparency, or the societal impacts of automated systems. The standardized tagging and metrics enable systematic analysis across large numbers of incidents.

Legal professionals and regulators working on AI-related cases or enforcement actions who need documented precedents and evidence of harm patterns in specific AI application areas.

Practical applications in your work

Use the repository's search and filtering capabilities to build sector-specific risk profiles—for example, pulling all documented incidents in AI-powered medical diagnostics to inform healthcare AI governance frameworks. The standardized incident categories make it possible to identify patterns that might not be visible when looking at individual cases.

For policy development, the repository's accountability tracking becomes invaluable. You can analyze how different regulatory approaches have worked in practice by examining post-incident responses across jurisdictions. This helps inform decisions about whether voluntary self-regulation, mandatory reporting requirements, or other policy tools are most effective.

The timeline data enables trend analysis—are certain types of AI incidents becoming more or less common? Are response times improving? Are particular sectors or demographics consistently affected? These patterns provide the empirical foundation needed for evidence-based AI governance.

Getting the most from the data

The repository's strength lies in its structured approach to incident documentation, but extracting insights requires understanding its categorization system. Spend time exploring the tagging taxonomy before diving into analysis—understanding how incidents are classified will help you ask better questions of the data.

Pay attention to the accountability metrics embedded in each incident record. These track not just what went wrong, but institutional responses, fixes implemented, and ongoing impacts. This longitudinal data often reveals more about AI governance gaps than the initial incident itself.

Consider combining repository data with other sources for comprehensive analysis. While the AIAAIC Repository excels at structured incident documentation, pairing it with regulatory databases, academic research, or industry reports can provide fuller context for the patterns you identify.