Towards an AI Accountability Policy

• URL: http://arxiv.org/abs/2307.13658v2
• Date: Wed, 26 Feb 2025 18:17:19 GMT
• Title: Towards an AI Accountability Policy
• Authors: Przemyslaw Grabowicz, Adrian Byrne, Cyrus Cousins, Nicholas Perello, Yair Zick,
• Abstract summary: We examine how high-risk technologies have been successfully regulated at the national level.<n>We propose a tiered system of explainability and benchmarking requirements for commercial AI systems.
• Score: 16.59829043755575
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We propose establishing an office to oversee AI systems by introducing a tiered system of explainability and benchmarking requirements for commercial AI systems. We examine how complex high-risk technologies have been successfully regulated at the national level. Specifically, we draw parallels to the existing regulation for the U.S. medical device industry and the pharmaceutical industry (regulated by the FDA), the proposed legislation for AI in the European Union (the AI Act), and the existing U.S. anti-discrimination legislation. To promote accountability and user trust, AI accountability mechanisms shall introduce standarized measures for each category of intended high-risk use of AI systems to enable structured comparisons among such AI systems. We suggest using explainable AI techniques, such as input influence measures, as well as fairness statistics and other performance measures of high-risk AI systems. We propose to standardize internal benchmarking and automated audits to transparently characterize high-risk AI systems. The results of such audits and benchmarks shall be clearly and transparently communicated and explained to enable meaningful comparisons of competing AI systems via a public AI registry. Such standardized audits, benchmarks, and certificates shall be specific to intended high-risk use of respective AI systems and could constitute conformity assessment for AI systems, e.g., in the European Union's AI Act.

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