AI Risk Categorization Decoded (AIR 2024)

  • Zeng Y
  • Klyman K
  • Zhou A
  • et al.
N/ACitations
Citations of this article
1Readers
Mendeley users who have this article in their library.

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.

Cite

CITATION STYLE

APA

Zeng, Y., Klyman, K., Zhou, A., Yang, Y., Pan, M., Jia, R., … Li, B. (2024). AI Risk Categorization Decoded (AIR 2024). SuperIntelligence - Robotics - Safety & Alignment, 1(1). https://doi.org/10.70777/si.v1i1.10603

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free