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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55.03.03 · Risk Sub-Category
Increased power concentration and inequality
Developments in AI enable actors to undermine democratic processes
"Developments in AI are giving companies and governments more control over individuals’ lives than ever before, and may possibly be used to undermine democratic processes. We are already seeing how the collection of large amounts of personal data can be used to surveil and influence populations, for example the use of facial recognition technology to surveil Uighur and other minority populations in China [66]. Further advances in language modelling could also be used to develop tools that can effectively persuade people of certain claims [42]."
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55.04.02 · Risk Sub-Category
Worsened epistemic processes for society
AI is used to scale up production of false and misleading information
"At the same time, we are seeing how AI can be used to scale up the production of convincing yet false or misleading information online (e.g. via image, audio, and text synthesis models like BigGAN [6] and GPT-3 [7])."
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55.04.03 · Risk Sub-Category
Worsened epistemic processes for society
AI's persuasive capabilities are misused to gain influence and promote harmful ideologies
"As AI capabilities advance, they may be used to develop sophisticated persuasion tools, such as those that tailor their communication to specific users to persuade them of certain claims [42]. While these tools could be used for social good— such as New York Times’ chatbot that helps users to persuade people to get vaccinated against Covid-19 [27]—there are also many ways they could be misused by self-interested groups to gain influence and/or to promote harmful ideologies."
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.