AI Risk Categorization Decoded (AIR 2024)

From Government Regulations to Corporate Policies

Authors

  • Yi Zeng Virginia Tech
  • Kevin Klyman Stanford University; Harvard University
  • Andy Zhou Lapis Labs; University of Illinois Urbana-Champaign
  • Yu Yang Virtue AI; University of California, Los Angeles
  • Minzhou Pan Virtue AI; Northeastern University
  • Ruoxi Jia Virginia Tech
  • Dawn Song Virtue AI; University of California, Berkeley
  • Percy Liang Stanford University
  • Bo Li University of Chicago

Keywords:

ai risks, agi risks, ai risks taxonomy, agi governance, corporate ai, ai governance, corporate ai policy

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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AI Risk Decoded-Table2

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Published

2024-10-09

How to Cite

Zeng, Y., Klyman, K., Zhou, A., Yang, Y., Pan, M., Jia, R., … Li, B. (2024). AI Risk Categorization Decoded (AIR 2024): From Government Regulations to Corporate Policies. AGI - Artificial General Intelligence - Robotics - Safety & Alignment, 1(1). Retrieved from https://agi-rsa.com/index.php/agi/article/view/10603