AI Risk Taxonomy
A comprehensive classification of 1,700+ AI risks across causal dimensions and 7 thematic domains.
Responsibility domains
6 domains and 24 sub-domains mapping AI risks according to the fruggr Responsibility Framework, aligned with NIST AI RMF and ISO 42001.
AI Risk Taxonomy
A comprehensive classification of 1,700+ AI risks across causal dimensions and 7 thematic domains.
MIT AI Risk Repository1700+ risks · 65 frameworks · 17k+ records screened
Who causes the risk?
AI system42%
Human actor45%
Ambiguous / systemic13%
Was it intentional?
Intentional34%
Unintentional52%
Unclear14%
Discrimination & Toxicity
312 risks
1.1 Unfair discrimination and misrepresentation
1.2 Exposure to toxic content
1.3 Unequal performance across groups
Privacy & Security
248 risks
2.1 Compromise of privacy
2.2 AI system security vulnerabilities and attacks
Misinformation
198 risks
3.1 False or misleading information
3.2 Pollution of information ecosystem
Malicious Actors & Misuse
276 risks
4.1 Disinformation, surveillance, and influence at scale
4.2 Fraud, scams, and targeted manipulation
4.3 Cyberattacks, weapons development, and mass harm
Human-Computer Interaction
142 risks
5.1 Overreliance and unsafe use
5.2 Loss of human agency and autonomy
Socioeconomic & Environmental
287 risks
6.1 Power centralization and unfair distribution of benefits
6.2 Increased inequality and decline in employment quality
6.3 Economic and cultural devaluation of human effort
6.4 Competitive dynamics
6.5 Governance failure
6.6 Environmental harm
AI System Safety, Failures & Limitations
237 risks
7.1 AI pursuing goals in conflict with human values
7.2 AI possessing dangerous capabilities
7.3 Lack of capability or robustness
7.4 Lack of transparency or interpretability
7.5 AI welfare and rights
7.6 Multi-agent risks
OECD AI Policy Observatory
Global policy tracking, trustworthy AI principles, and 970 governance tools catalogued.
Source: OECD.AI Policy Observatory, 2025
0+
Jurisdictions tracked
0
Countries adhering
0+
Incidents monitored
0
Tools & metrics
0
Thematic areas
OECD AI Principles (values)
1Inclusive growth, sustainable development and well-being
2Human rights and democratic values, including fairness and privacy
3Transparency and explainability
4Robustness, security and safety
5Accountability
AI lifecycle stages covered
Plan & designCollect & process dataBuild & interpret modelVerify & validateDeployOperate & monitor
Trustworthy AI objectives
SafetyTransparencyRobustnessFairnessPrivacyHuman Agency & ControlExplainabilityDigital SecurityData Governance & Traceability