MIT AI Risk Repository

Summary

The MIT AI Risk Repository stands out as one of the most comprehensive and academically rigorous databases for AI risk identification and classification. Unlike scattered risk assessments or high-level frameworks, this repository synthesizes thousands of risk scenarios from peer-reviewed research, regulatory documents, and industry incident reports into a searchable, structured taxonomy. What makes it particularly valuable is its evidence-based approach—every risk category is backed by real-world examples and academic citations, making it an authoritative reference for both researchers and practitioners building risk management programs.

What makes this different

Most AI risk frameworks focus on broad categories like "bias" or "safety," but the MIT repository drills down to granular risk scenarios with specific contexts. Instead of just listing "algorithmic bias" as a concern, you'll find subcategories like "historical bias amplification in hiring algorithms" or "demographic parity violations in credit scoring models." Each entry includes the source literature, affected stakeholders, and potential severity levels.

The repository also captures emerging risks that haven't yet made it into formal standards or regulations. The research team continuously scans new publications and incident reports, making this a living document that evolves with the field rather than a static checklist.

Key features and structure

The repository organizes risks across six primary dimensions: technical failures, societal harms, economic disruptions, security vulnerabilities, governance gaps, and existential concerns. Each risk entry includes:

• Risk definition and scope: Clear boundaries of what constitutes this specific risk
• Evidence base: Academic papers, incident reports, and policy documents that document this risk
• Manifestation examples: Real-world cases where this risk has materialized
• Stakeholder impact: Who gets affected and how
• Mitigation landscape: Current approaches and their effectiveness

The search functionality allows filtering by industry sector, AI system type, development stage, and severity level. You can also view risk interdependencies—how certain risks cascade into others or share common root causes.

Who this resource is for

AI risk managers and compliance teams will find this invaluable for comprehensive risk assessments and gap analyses against existing mitigation strategies. The granular categorization helps identify blind spots in current risk management approaches.

Researchers and academics can use it as a systematic literature review tool and a foundation for identifying under-researched risk areas. The citation tracking also helps map the evolution of risk understanding over time.

Product teams and AI developers benefit from the contextualized examples that help translate abstract risk concepts into concrete scenarios relevant to their systems. The industry-specific filtering makes it practical for targeted risk assessment.

Policy makers and regulators can leverage the evidence base to understand which risks have strong empirical support versus those that remain theoretical, informing priority-setting for regulatory attention.

Getting the most value

Start with the taxonomy overview to understand the risk landscape, then drill down into categories most relevant to your use case. The "risk pathway" visualizations are particularly useful for understanding how technical failures can cascade into societal harms.

For risk assessment exercises, use the repository's severity ratings and stakeholder impact analyses to prioritize which risks warrant immediate attention versus longer-term monitoring. The mitigation landscape sections can help benchmark your current approaches against emerging best practices.

The repository works well in conjunction with operational frameworks like NIST AI RMF—use MIT's granular risk identification to populate the "identify" function, then apply NIST's governance processes for management and response.

Limitations to keep in mind

The repository's academic foundation means it may lag behind rapidly emerging risks in commercial AI applications. The research publication cycle can create a 6-12 month delay before new risk patterns appear in the database.

Coverage is also uneven across domains—there's extensive documentation of risks in hiring, lending, and autonomous systems, but less comprehensive coverage of risks in emerging applications like generative AI or AI-assisted scientific discovery.

The global scope means some risks may be more relevant in certain regulatory jurisdictions than others, requiring local contextualization for practical application.