New Research Resource From MIT CSAIL: AI Risk Repository

From MIT CSAIL:

The adoption of AI is rapidly increasing; census data shows a significant (47%) rise in AI usage within US industries, jumping from 3.7% to 5.45% between September 2023 and February 2024. However, a comprehensive review from researchers at MIT CSAIL and  MIT FutureTech has uncovered critical gaps in existing AI risk frameworks. Their analysis reveals that even the most thorough individual framework overlooks approximately 30% of the risks identified across all reviewed frameworks.

To help address this, they have collaborated with colleagues from the University of Queensland, Future of Life Institute, KU Leuven, and Harmony Intelligence, to release the first-ever AI Risk Repository: a comprehensive and accessible living database of 700+ risks posed by AI that will be expanded and updated to ensure that it remains current and relevant.

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After searching several academic databases, engaging experts, and retrieving more than 17,000 records, the researchers identified 43 existing AI risk classification frameworks. From these, they extracted more than 700 risks. They then used approaches that they developed from two existing frameworks to categorize each risk by cause (e.g., when or why it occurs), risk domain (e.g., “Misinformation”), and risk subdomain (e.g., “False or misleading information”).

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“The AI Risk Repository is, to our knowledge, 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,” says Dr. Neil Thompson, head of the MIT FutureTech Lab and one of the lead researchers on the project. “We are starting with a comprehensive checklist, to help us understand the breadth of potential risks. We plan to use this to identify shortcomings in organizational responses. For instance, if everyone focuses on one type of risk while overlooking others of similar importance, that’s something we should notice and address.”

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Direct to AI Risk Repository

Direct to AI Risk Repository Intro Video

See Also: AI Incident Database

Filed under: News, Open Access