Partnership on AI Incident Database

Summary

The Partnership on AI Incident Database is a groundbreaking global repository that transforms how we understand AI failures. Rather than letting AI incidents disappear into corporate silence or academic obscurity, this database creates a permanent public record of when AI systems cause harm. With over 2,000+ documented incidents spanning from algorithmic bias in hiring to autonomous vehicle crashes, it's become the definitive resource for understanding AI's real-world risks and failure patterns.

What makes this database unique

Unlike theoretical risk assessments or vendor whitepapers, this database documents actual incidents where AI systems have caused documented harm to individuals, communities, or society. Each entry includes detailed incident descriptions, sources, and metadata that allows for pattern recognition across industries and AI applications.

The database goes beyond simple incident logging—it creates a taxonomy of AI harm types, enabling researchers to identify systemic issues rather than isolated failures. This approach has revealed concerning patterns, such as how facial recognition systems consistently fail for darker-skinned individuals across different vendors and use cases.

The anatomy of an AI incident report

Each incident in the database follows a structured format that makes it invaluable for research and prevention:

• Incident description: What happened, when, and where
• AI system details: Type of algorithm, training data, intended use
• Harm classification: Physical, economic, social, or psychological impacts
• Affected parties: Who was harmed and how
• Source documentation: News reports, court filings, research papers
• Related incidents: Connected cases showing patterns

This standardization allows researchers to query incidents by harm type, industry, AI technique, or affected demographic groups.

Who this resource is for

AI researchers and academics studying algorithmic bias, safety, or ethics will find this an essential resource for empirical research on AI failures and their societal impacts.

Risk management professionals can use incident patterns to inform risk assessments, especially when deploying similar AI systems or entering new application domains.

Policy makers and regulators rely on this database to understand the landscape of AI harms when crafting legislation or enforcement priorities.

Journalists and civil society organizations investigating AI accountability use the database to contextualize individual cases within broader patterns of algorithmic harm.

AI practitioners and engineers can learn from documented failures to improve their own system design and testing practices.

Mining the database for insights

The true power of this resource lies in its ability to reveal systemic patterns:

Temporal analysis: Track whether certain types of AI incidents are increasing or decreasing over time, and identify emerging categories of harm.

Industry clustering: Discover which sectors experience the most AI incidents and what types of failures are most common in each domain.

Demographic impact assessment: Analyze which communities bear the disproportionate burden of AI system failures.

Technical failure patterns: Identify which AI techniques or implementation approaches are associated with higher incident rates.

The database's search and filtering capabilities allow users to slice the data by date ranges, keywords, incident types, or affected industries.

Watch out for

Reporting bias: The database relies on publicly reported incidents, meaning it likely underrepresents harms in sectors with less transparency or media coverage.

Definition challenges: What constitutes an "AI incident" can be subjective, and the database's inclusion criteria may not align with every user's definition of AI-related harm.

Incomplete information: Many incident reports lack technical details about the AI systems involved, limiting the ability to draw specific technical conclusions.

Lag time: There's often a delay between when incidents occur and when they're documented, particularly for incidents that only become public through legal proceedings.

FAQs

Is the database comprehensive? No database of this type can claim completeness. The Partnership on AI Incident Database captures publicly reported incidents, but many AI harms likely go unreported or undocumented, particularly in sectors with less transparency.

Can I contribute incidents? Yes, the database accepts submissions from the public. Each submission goes through a review process to ensure it meets the database's standards for documentation and verifiability.

How often is it updated? The database is continuously updated as new incidents are identified and verified. The team actively monitors news sources, academic publications, and user submissions.

Is the data available for research? Yes, the database provides various ways to access the data, including web interface browsing, search functionality, and data exports for academic research purposes.