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MIT AI Risk Repository

mit-ai-risk-repository (E383)
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---
title: MIT AI Risk Repository
description: "A comprehensive living database of 1,700+ AI risks extracted from 65+ published frameworks, organized using two taxonomies: a Causal Taxonomy (who/intent/timing) and a Domain Taxonomy (7 domains, 24 subdomains). Created by MIT FutureTech researchers, the repository provides a shared framework for industry, policymakers, and academics to monitor and manage AI risks."
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llmSummary: The MIT AI Risk Repository catalogs 1,700+ AI risks from 65+ frameworks into a searchable database with dual taxonomies (causal and domain-based). Updated quarterly since August 2024, it provides the first comprehensive public catalog of AI risks but is limited by framework extraction methodology and lacks quantitative risk assessments.
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## Quick Assessment

| Dimension | Assessment | Evidence |
|-----------|------------|----------|
| **Coverage** | Comprehensive | 1,700+ risks from 65+ frameworks |
| **Data Freshness** | Quarterly updates | Regular additions since August 2024 |
| **Accessibility** | High | Free access via Google Sheets/OneDrive |
| **Methodology** | Rigorous | Systematic review, expert consultation |
| **Target Audience** | Broad | Industry, policymakers, academics, auditors |
| **Maintenance** | Active | MIT FutureTech team |

## Project Details

| Attribute | Details |
|-----------|---------|
| **Name** | MIT AI Risk Repository |
| **Organization** | MIT FutureTech / MIT AI Risk Initiative |
| **Lead Researcher** | Peter Slattery |
| **Team** | Alexander Saeri, Michael Noetel, Jess Graham, Neil Thompson |
| **Website** | [airisk.mit.edu](https://airisk.mit.edu/) |
| **Paper** | [arXiv:2408.12622](https://arxiv.org/abs/2408.12622) |
| **License** | CC BY 4.0 |
| **Data Access** | Google Sheets, OneDrive |

## Overview

The [MIT AI Risk Repository](https://airisk.mit.edu/) is a living database cataloging over 1,700 AI risks extracted from 65+ published frameworks and taxonomies. It represents the first comprehensive attempt to curate, analyze, and organize AI risk frameworks into a publicly accessible, categorized database.

The repository serves multiple stakeholders:
- **Industry**: Identifying risks for product development and compliance
- **Policymakers**: Understanding the risk landscape for regulation
- **Academics**: Research foundation and gap analysis
- **Risk Evaluators**: Structured framework for auditing AI systems

### The Problem It Solves

Before the repository, AI risk knowledge was fragmented across dozens of separate frameworks, each with different terminology, scope, and categorization schemes. This fragmentation made it difficult to:

- Compare risks across frameworks
- Identify gaps in coverage
- Develop comprehensive risk management strategies
- Coordinate across organizations and jurisdictions

The repository provides a unified view, extracting risks from existing frameworks and organizing them using consistent taxonomies.

## Dual Taxonomy Structure

The repository uses two complementary classification systems:

### Causal Taxonomy

Classifies risks by **how, when, and why they occur**:

| Dimension | Categories | Examples |
|-----------|------------|----------|
| **Entity** | Human, AI | Who causes the risk |
| **Intentionality** | Intentional, Unintentional | Malicious vs. accidental |
| **Timing** | Pre-deployment, Post-deployment | When risk manifests |

This enables filtering by causal pathway—e.g., "show me all unintentional AI-caused post-deployment risks."

### Domain Taxonomy

Organizes risks into **7 domains and 24 subdomains**:

| Domain | Subdomains | Examples |
|--------|------------|----------|
| **Discrimination & Toxicity** | Bias, unfairness, harmful content | Algorithmic discrimination |
| **Privacy & Security** | Data breaches, surveillance | Model inversion attacks |
| **Misinformation** | <EntityLink id="E96">Deepfakes</EntityLink>, manipulation | AI-generated <EntityLink id="E102">disinformation</EntityLink> |
| **Malicious Actors & Misuse** | Cyberattacks, weapons | <EntityLink id="E35">Autonomous weapons</EntityLink> |
| **Human-Computer Interaction** | Overreliance, manipulation | <EntityLink id="E32">Automation bias</EntityLink> |
| **Socioeconomic & Environmental** | Job displacement, energy use | Labor market disruption |
| **AI System Safety** | Failures, alignment issues | <EntityLink id="E151">Goal misgeneralization</EntityLink> |

The April 2025 update added a new subdomain: **multi-agent risks**.

## Database Evolution

| Version | Date | Frameworks | Risks | Key Additions |
|---------|------|------------|-------|---------------|
| **v1** | August 2024 | 43 | ≈770 | Initial release |
| **v2** | December 2024 | 56 | ≈1,070 | +13 frameworks, +300 risks |
| **v3** | April 2025 | 65+ | 1,612 | +22 frameworks, multi-agent subdomain |
| **Current** | December 2025 | 74+ | 1,700+ | Ongoing additions |

## Methodology

### Framework Identification

Researchers used multiple methods to identify source frameworks:

1. **Systematic search strategy**: Academic databases, grey literature
2. **Forward/backward searching**: References within identified frameworks
3. **Expert consultation**: Input from AI safety researchers

### Risk Extraction

For each framework:
1. Extract individual risk categories
2. Normalize terminology to consistent vocabulary
3. Classify using both taxonomies
4. Link to source material

### Quality Assurance

- **Best fit framework synthesis**: Iterative refinement of taxonomies
- **Expert review**: Validation by AI safety researchers
- **Regular updates**: Quarterly incorporation of new frameworks

## Use Cases

### Risk Management

Organizations use the repository to:

| Use Case | Application |
|----------|-------------|
| **Gap Analysis** | Identify risks not covered by current policies |
| **Compliance Mapping** | Match internal categories to regulatory frameworks |
| **Audit Checklists** | Structured approach to AI system review |
| **Training Materials** | Comprehensive risk awareness resources |

### Research

Academics leverage the database for:

- **Systematic reviews**: Foundation for literature analysis
- **Taxonomy development**: Building on established categorization
- **Comparative analysis**: Understanding how frameworks differ
- **Trend identification**: Tracking emerging risk categories

### Policy Development

Policymakers reference the repository for:

- **Regulatory scope**: Understanding what risks exist to regulate
- **<EntityLink id="E171">International coordination</EntityLink>**: Common vocabulary across jurisdictions
- **Framework comparison**: Evaluating existing approaches

## Related Work: Risk Mitigations

In December 2025, the MIT team extended the repository with a **Risk Mitigations** database, mapping interventions to the risks they address. This enables:

- Identifying which risks lack adequate mitigations
- Comparing mitigation strategies across domains
- Prioritizing intervention research

## Strengths and Limitations

### Strengths

| Strength | Evidence |
|----------|----------|
| **Comprehensive coverage** | 1,700+ risks from 65+ frameworks |
| **Rigorous methodology** | Systematic review, expert validation |
| **Dual taxonomy** | Enables multiple analysis perspectives |
| **Regular updates** | Quarterly additions of new frameworks |
| **Open access** | CC BY 4.0 license, free database access |
| **Institutional backing** | MIT credibility and resources |

### Limitations

| Limitation | Impact |
|------------|--------|
| **Framework-dependent** | Only captures risks identified in published sources |
| **No quantification** | Doesn't assess likelihood or severity |
| **Extraction methodology** | Interpretation decisions affect categorization |
| **English-language focus** | May miss non-English frameworks |
| **Static snapshots** | Individual risks don't track evolution over time |
| **Aggregation challenges** | Similar risks may appear duplicated across frameworks |

## Comparison with Other Resources

| Resource | Focus | Coverage | Updates |
|----------|-------|----------|---------|
| **MIT AI Risk Repository** | Comprehensive catalog | 1,700+ risks | Quarterly |
| **NIST AI RMF** | Risk management process | Process-focused | Periodic |
| **<EntityLink id="E127">EU AI Act</EntityLink> Categories** | Regulatory compliance | Regulatory risks | Legislative cycle |
| **<EntityLink id="E382">AISafety.info</EntityLink>** | <EntityLink id="E598">Public education</EntityLink> | Conceptual | Community-driven |
| **This Wiki (Longterm)** | Prioritization analysis | X-risk focused | Ongoing |

## External Links

- [MIT AI Risk Repository](https://airisk.mit.edu/)
- [arXiv Paper](https://arxiv.org/abs/2408.12622)
- [MIT Sloan Coverage](https://mitsloan.mit.edu/ideas-made-to-matter/new-database-details-ai-risks)
- [MIT IDE Announcement](https://ide.mit.edu/insights/mit-researchers-create-an-open-ai-risk-repository/)
- [VentureBeat Coverage](https://venturebeat.com/ai/mit-releases-comprehensive-database-of-ai-risks)
- [Risk Mitigations Blog Post](https://airisk.mit.edu/blog/mapping-ai-risk-mitigations)