AI Risk Categorization Decoded (AIR 2024)

Yi Zeng · Kevin Klyman · Andy Zhou · Yang Yu · Minzhou Pan · Ruoxi Jia +3 more

19 citations0/0 files storedSuperIntelligence - Robotics - Safety & Alignment

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Ethics and Social Impacts of AI Digitalization, Law, and Regulation Safety Systems Engineering in Autonomy

Abstract

We present a comprehensive AI risk taxonomy derived from eight government poli- cies from the European Union, United States, and China and 16 company policies worldwide, making a significant step towards establishing a unified language for generative AI safety evaluation. We identify 314 unique risk categories, organized into a four-tiered taxonomy. At the highest level, this taxonomy encompasses System & Operational Risks, Content Safety Risks, Societal Risks, and Legal & Rights Risks. The taxonomy establishes connections between various descriptions and approaches to risk, highlighting the overlaps and discrepancies between public and private sector conceptions of risk. By providing this unified framework, we aim to advance AI safety through information sharing across sectors and the promotion of best practices in risk mitigation for generative AI models and systems.

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2008The qualitative content analysis process 2014Qualitative Content Analysis: Theoretical Background and Procedures 2023Regulating ChatGPT and other Large Generative AI Models 2023Digital Empires 2023Sociotechnical Harms of Algorithmic Systems: Scoping a Taxonomy for Harm Reduction 2022Governing artificial intelligence in China and the European Union: Comparing aims and promoting ethical outcomes 2022Artificial intelligence with American values and Chinese characteristics: a comparative analysis of American and Chinese governmental AI policies 2022Behavioral Use Licensing for Responsible AI

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