AI Incident Database Dataset

Summary

The AI Incident Database Dataset transforms the messy, anecdotal world of AI failures into structured data that researchers and practitioners can actually analyze. Instead of relying on scattered news reports and individual case studies, this dataset from the Responsible AI Collaborative provides a systematic way to study patterns in AI incidents—from algorithmic bias in hiring systems to autonomous vehicle crashes to facial recognition errors. By standardizing how we document and categorize AI harms, this resource enables evidence-based approaches to AI safety and risk management.

What's actually in the dataset

The dataset contains structured records of real-world AI incidents, each tagged with multiple attributes including:

• Harm categories: Physical harm, economic loss, privacy violations, discrimination, manipulation, and more
• AI system details: Technology type (computer vision, NLP, ML classification, etc.), deployment context, and scale
• Stakeholder information: Deployers, developers, harmed parties, and affected populations
• Incident characteristics: Severity, intentionality, geographic location, and temporal factors
• Response data: Whether incidents triggered investigations, policy changes, or system modifications

Each incident links back to source documentation, media reports, academic papers, or official investigations, maintaining transparency about data provenance.

The backstory: From news clippings to structured analysis

Traditional AI incident tracking relied on ad-hoc collections of news articles and blog posts—useful for awareness but nearly impossible to analyze systematically. The Partnership on AI launched the original AI Incident Database in 2020 to create a more rigorous approach, similar to how aviation and healthcare track safety incidents. The Responsible AI Collaborative now maintains and expands this work, applying consistent taxonomies and data structures that enable researchers to identify patterns, test hypotheses, and measure trends in AI safety over time.

Who this resource is for

AI safety researchers conducting empirical studies on failure modes, harm patterns, or the effectiveness of safety interventions across different AI applications.

Policy analysts and regulators who need evidence-based insights about AI risks to inform governance frameworks, enforcement priorities, or regulatory impact assessments.

Risk management teams at organizations deploying AI systems, particularly those seeking to benchmark their risk assessment approaches against real-world incident patterns.

Academic researchers in computer science, law, policy, or social sciences studying AI's societal impacts with quantitative methods rather than purely theoretical approaches.

Standards developers working on AI safety frameworks who need empirical data to validate risk categories, severity scales, or mitigation strategies.

Getting the most out of this dataset

Start by exploring the discovery interface at incidentdatabase.ai to understand the incident taxonomy and data structure before downloading. The web interface lets you filter by harm types, technologies, or time periods to get a sense of what patterns might be worth investigating.

For quantitative analysis, focus on incidents with complete attribute data rather than trying to use every record—data quality varies based on available source documentation. The most robust analyses often combine multiple attributes (technology type + harm category + deployment context) rather than looking at single variables in isolation.

Consider temporal factors carefully when analyzing trends. The dataset reflects both actual changes in AI incident rates and changes in reporting, media attention, and data collection practices over time.

Watch out for

This is observational data based on publicly reported incidents, not a comprehensive census of all AI harms. High-profile failures are overrepresented compared to routine problems that don't generate media coverage. Consumer-facing applications appear more frequently than enterprise or government systems where incidents may remain confidential.

The structured categories impose boundaries on messy real-world situations—some incidents span multiple harm types or involve complex causal chains that don't fit neatly into predefined taxonomies.

Data completeness varies significantly across incidents depending on available source material. Legal restrictions, corporate confidentiality, and investigation timelines mean some incident details may never become public.

FAQs

Can I use this dataset for commercial research? Yes, the dataset is publicly available for research purposes, though you should verify current licensing terms and cite appropriately.

How often is the dataset updated? New incidents are added regularly as they're identified and verified, though there's no fixed update schedule. Recent incidents may take time to appear as verification requires multiple sources.

Does this cover AI incidents outside the US and Europe? The database aims for global coverage but reflects the geographic distribution of publicly reported incidents and English-language sources, which may underrepresent some regions.

How do you distinguish between AI incidents and general software failures? Incidents must involve AI/ML systems as a primary component, but the boundaries can be fuzzy with complex systems. The database tends toward inclusion when AI plays a significant role in the failure mode.