Towards AI Transparency and Accountability: A Global Framework for Exchanging Information on AI Systems
This work addresses the problem of regulatory fragmentation and overregulation in AI for policymakers and industry, offering a scalable framework to enhance transparency and accountability.
The paper tackles the challenge of creating effective AI transparency and accountability regulations by proposing a global standard for exchanging information about AI systems, which enables compatibility with diverse local rules and includes automated assessments and AI cards for public comparison.
We propose that future AI transparency and accountability regulations are based on an open global standard for exchanging information about AI systems, which allows co-existence of potentially conflicting local regulations. Then, we discuss key components of a lightweight and effective AI transparency and/or accountability regulation. To prevent overregulation, the proposed approach encourages collaboration between regulators and industry to create a scalable and cost-efficient mutually beneficial solution. This includes using automated assessments and benchmarks with results transparently communicated through AI cards in an open AI register to facilitate meaningful public comparisons of competing AI systems. Such AI cards should report standardized measures tailored to the specific high-risk applications of AI systems and could be used for conformity assessments under AI transparency and accountability policies such as the European Union's AI Act.