AI Incident Database

Summary

The AI Incident Database represents the most comprehensive public archive of documented AI system failures and their real-world consequences. Unlike theoretical risk assessments or abstract harm categories, this database catalogs actual incidents where AI systems have caused measurable damage—from algorithmic bias in hiring systems to autonomous vehicle accidents to content moderation failures at scale. Each entry provides detailed incident reports, timelines, and analysis that transform abstract AI risks into concrete case studies with real victims and measurable impacts.

The Backstory

Launched in 2021 by the Responsible AI Collaborative, the database emerged from a critical gap in AI governance: while industries like aviation and nuclear power maintain rigorous incident reporting systems that drive safety improvements, the AI field had no equivalent learning mechanism. The database applies established incident analysis methodologies to AI systems, creating a systematic way to identify patterns, root causes, and prevention strategies across the rapidly evolving AI landscape.

What's Inside the Database

The database categorizes incidents across multiple dimensions that reveal systemic patterns:

Incident Types: From discriminatory algorithmic decisions and privacy violations to physical safety failures and economic manipulation. Each incident receives detailed classification tags that enable pattern recognition across seemingly unrelated events.

Stakeholder Analysis: Every entry identifies the deploying organizations, affected populations, and reporting entities, revealing how AI harms often follow predictable power dynamics and disproportionately impact vulnerable communities.

Timeline Documentation: Incidents include discovery dates, public disclosure timelines, and resolution status, exposing how AI harms can persist undetected for extended periods before becoming visible.

Impact Assessment: Quantified damages where available, including affected user counts, financial costs, and documented social harms, providing concrete data on AI risk materialization.

Real-World Applications

Risk Assessment Teams use the database to ground theoretical threat models in documented reality, identifying which risks actually materialize and under what conditions. The incident patterns help prioritize mitigation efforts based on observed failure modes rather than hypothetical scenarios.

Product Development Teams search for incidents involving similar AI systems or deployment contexts to identify potential failure modes early in development. The database serves as a "lessons learned" repository that prevents repeating documented mistakes.

Policy Researchers analyze incident trends to identify regulatory gaps and design evidence-based governance frameworks. The database provides the empirical foundation for AI policy debates that often rely on speculation about potential harms.

Audit and Compliance Functions reference relevant incidents during AI system assessments, using documented cases to justify specific testing requirements or risk mitigation measures.

Who This Resource Is For

AI Safety Researchers conducting empirical analysis of AI system failures and developing evidence-based safety methodologies

Risk Management Professionals in organizations deploying AI systems who need concrete examples of materialized risks to inform threat modeling and control design

Legal and Compliance Teams documenting due diligence efforts and referencing precedent cases in AI governance frameworks

Policy Analysts and Regulators seeking empirical evidence of AI harms to inform regulatory development and enforcement priorities

Journalists and Civil Society Organizations investigating AI system impacts and holding deploying organizations accountable for documented harms

Getting Started

Begin with the database's taxonomy browser to understand how incidents are categorized across harm types, AI system categories, and affected stakeholders. Use the search functionality to find incidents relevant to your specific AI system type or deployment context.

For systematic analysis, download the structured incident data to identify patterns relevant to your use case. The database provides both human-readable incident reports and machine-readable metadata for quantitative analysis.

Cross-reference incidents with your organization's AI system inventory to identify systems with documented failure modes in similar deployments, then use these cases to enhance your risk assessments and testing protocols.