MIT AI Risk Repository
Browse AI risks
3 risk entries extracted from 74 frameworks, coded by domain, subdomain, causal entity, intent and timing. Filter, then export the current selection with its licence and citation attached.
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"The integration of general-purpose AI into high-frequency trading, market-making, or systemic risk management could exacerbate systemic risk by exhibiting unexpected behavioral patterns during market stress. Moreover, the concentration of a few homogeneous foundation models across financial institutions may foster correlated decision-making and herd-following behaviors. The widespread adoption of AI agents could also amplify volatility through emergent phenomena from multi-agent interactions.23 All of these could precipitate a cascading global-scale financial system instability, with potentia
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"Multiple autonomous AI agents able to establish collaborative relationships through explicit communication or implicit behavioral consistency, forming decentralized decision networks, jointly executing complex tasks, achieving goals difficult for individual agents to complete, and able to dynamically adjust role divisions to adapt to changing environments."
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"Multiple agents tend to coordinate actions through covert means to maximize common interests (possibly harming third-party interests or evading regulation), even if individual agents are designed with safety constraints, their collusive behavior may still trigger systemic risks such as market manipulation or cascading failures that are difficult to detect and mitigate, and may develop specialized communication protocols to avoid monitoring."
Disclaimer: informational only, not legal advice. Verify every claim against the linked official sources and consult a qualified lawyer before acting.
Frequently asked questions
Where do these risk entries come from?
The MIT AI Risk Repository, which extracts risks from dozens of published frameworks, taxonomies and papers and codes each one by domain and subdomain, and by a causal taxonomy of entity, intent and timing. This is a browseable copy, attributed and openly licensed, not original research.
What do entity, intent and timing mean?
They are the causal coding. Entity is whether a human or the AI system is the cause; intent is whether the harm was intentional or not; timing is whether it arises before or after deployment. Together they let you separate misuse from malfunction.
Can I export the results?
Yes, any filtered set exports as CSV or JSON, and every export carries the upstream source, licence and citation with it, because attribution is a condition of the licence rather than a courtesy.
What is the MIT AI Risk Repository?
A living database of AI risks extracted from published frameworks, taxonomies and papers, classified by a causal taxonomy of entity, intent and timing and by a domain taxonomy of seven domains and 24 subdomains. It is published by the MIT AI Risk Initiative under CC BY 4.0.