AI Risk Framework Gaps

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AI Risk Repository

TechXplore

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Global AI adoption is outpacing risk understanding, researchers warn

Summary

Nutrition label

90% Informative

A new analysis of AI -related risks finds significant gaps in our understanding of the risks posed by AI .

The most frequently addressed risk domains included "AI system safety, failures, and limitations" "Socioeconomic and environmental harms" ( 73% ) "Discrimination and toxicity" ( 71% ) "Privacy and security" ( 68% ) "Malicious actors and misuse" received comparatively less attention.

On average, frameworks mentioned just 34% of the 23 risk subdomains identified.

MIT 's AI Risk Repository is the first attempt to rigorously curate, analyze, and extract AI risk frameworks into a publicly accessible, comprehensive, extensible, and categorized risk database.

It is part of a larger effort to understand how we are responding to AI risks and to identify if there are gaps in our current approaches.

The repository is freely available online to download, copy and use.

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Neutral language

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formal

Language

English

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not offensive