MIT AI Risk Repository
The MIT AI Risk Repository is a major research initiative created to provide the world’s most comprehensive, structured, and unified resource on risks posed by artificial intelligence. It functions as a living, continuously updated database of AI risks, taxonomies, and documented sources, developed by the MIT AI Risk Initiative / MIT FutureTech Group.
It is publicly accessible at airisk.mit.edu.
1. What the MIT AI Risk Repository Is
According to MIT, the AI Risk Repository is:
- A centralized, living database of AI-related risks, currently listing 700–1700+ risks depending on the version referenced (MIT's web version lists 1700+, while the academic paper documents 777 risks).
- Compiled from dozens of academic, government, and industry AI frameworks (43–74 frameworks, depending on the version).
- Designed to create a shared vocabulary for researchers, policymakers, auditors, and companies when discussing AI risks.
- Open-access and designed to be extensible, meaning new risks can be added as the field evolves.
The repository aims to unify a fragmented AI governance landscape and support future policy, regulation, audits, and safe AI development practices.
2. Core Components of the Repository
MIT describes the repository as having three primary components:
A. The AI Risk Database
Contains:
- 700–1700+ documented AI risks
- Direct links to source material (papers, frameworks, reports)
- Quotes and page numbers verifying each risk
This database enables:
- Filtering risks by type, cause, domain, or scenario
- Downloading risks in formats like Google Sheets or OneDrive
- Reviewing evidence and citations for each risk
B. The Causal Taxonomy of AI Risks
This taxonomy classifies how a risk arises based on three dimensions:
1. Entity
- Human
- AI
- Other/ambiguous
2. Intentionality
- Intentional
- Unintentional
- Undefined
3. Timing
- Pre-deployment
- Post-deployment
- Unspecified
This answers:
Who caused the risk?
Was it intentional?
When does it arise?
C. The Domain Taxonomy of AI Risks
This organizes risks into 7 major domains and 23–24 subdomains.
The seven high-level domains are:
1. Discrimination & toxicity
2. Privacy & security
3. Misinformation
4. Malicious actors & misuse
5. Human-computer interaction issues
6. Socioeconomic & environmental impacts
7. AI system safety, failures & limitations
MIT notes, for example, that privacy and security risks appear in 70%+ of the reviewed frameworks, while risks such as AI rights and welfare appear in <1%.
3. How the Repository Was Created
The repository was built via a systematic meta-review of existing AI risk frameworks.
Researchers: Peter Slattery, Neil Thompson, and a multi-disciplinary MIT team. [ide.mit.edu], [arxiv.org]
The process involved:
1. Reviewing 43–74 AI governance documents
2. Extracting every explicit AI risk described
3. An expert consultation process
4. Creating high-level and mid-level taxonomies
5. Publishing the database and taxonomies openly
The academic paper describing this process is titled:
“The AI Risk Repository: A Comprehensive Meta‑Review, Database, and Taxonomy of Risks From Artificial Intelligence” (2024–2025).
4. Why the MIT AI Risk Repository Matters
A. Establishes a Shared Language
The AI governance ecosystem is fragmented. Different industries, researchers, and governments use inconsistent terminology. The MIT repository unifies them under one standard. [mitsloan.mit.edu]
B. Improves AI Safety and Compliance
Organizations can use the repository to:
- Identify relevant risks
- Prioritize risk mitigation
- Build audits and assessments
- Improve AI governance frameworks
C. Helps Policymakers
Regulators can more clearly understand:
- Where risks occur
- How common they are
- How they compare across industries
D. Tracks Underexplored Risk Categories
For example, MIT found:
- Privacy & security risks appear in >70% of risk frameworks
- Misinformation risks appear in only ~40%
- AI welfare/rights appear in <1%
This highlights research gaps.
E. Supports Research, Education, and Standardization
The repository is used for:
- Academic research
- Policy development
- Corporate risk audits
- Curriculum design
5. Examples of Risks Found in the Repository
The repository documents risks across many categories, including:
- Bias and discrimination in model outputs
- Privacy breaches/data leakage
- Deepfake misinformation
- AI-enabled cyberattacks
- Model hallucinations
- Autonomous system failures
- Socioeconomic displacement
- Environmental resource consumption
Each risk is paired with:
- Citations
- Exact quotes
- Evidence
- Categorization by taxonomy
6. How to Use the MIT AI Risk Repository
MIT suggests several uses:
- Search for risks relevant to a specific AI system
- Explore causal and domain factors to build risk models
- Build governance frameworks and compliance plans
- Teach AI risk management in educational settings
- Monitor emerging risks as the database updates
7. Strengths and Limitations (Based on Research Commentary)
Strengths
- Open-access, transparent, regularly updated
- Most comprehensive resource of its kind
- Useful taxonomies (causal and domain-based)
- Unified framework that integrates 700+ risks
- Valuable for practical AI governance
Limitations
- Some risks may be high-level or ambiguous
- Interpretation depends on user expertise
- Coverage of novel or speculative risks is still evolving
- Some domains are underrepresented (e.g., AI rights)
8. Summary
The MIT AI Risk Repository is one of the most important AI governance tools available today. It combines:
- A living database of 700–1700+ AI risks
- A causal taxonomy explaining how risks arise
- A domain taxonomy categorizing risk areas
- Full citations and evidence
- Open-access resources for researchers, businesses, auditors, and policymakers
Its purpose is to standardize AI risk vocabulary, support governance, and improve global understanding of AI risks in a rapidly evolving field.
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