"Death by a thousand taxonomies?": AI Risk Classification In Practice

An empirical study based on interviews with researchers and practitioners reveals that existing Sociotechnical Outcome Taxonomies (SOTs) for AI risk classification are poorly integrated into practical AI governance processes. This weak integration is attributed to specific features of SOT design and their application, despite the foundational importance of risk classification for regulators, technology firms, and policymakers.

Effective classification of AI risks is fundamental for developing robust governance frameworks and ensuring responsible AI deployment across various sectors. The identified shortcomings in the practical application of risk taxonomies highlight a critical gap that could impede effective regulation, policy-making, and organizational risk management in AI development and adoption.

AI is implicated in a broad range of harms, from unsafe user interactions to societal-wide consequences.

Forward consideration, not a verified fact.