Towards Regulatable AI Systems: Technical Gaps and Policy Opportunities. (arXiv:2306.12609v1 [cs.AI])
By: <a href="http://arxiv.org/find/cs/1/au:+Shen_X/0/1/0/all/0/1">Xudong Shen</a>, <a href="http://arxiv.org/find/cs/1/au:+Brown_H/0/1/0/all/0/1">Hannah Brown</a>, <a href="http://arxiv.org/find/cs/1/au:+Tao_J/0/1/0/all/0/1">Jiashu Tao</a>, <a href="http://arxiv.org/find/cs/1/au:+Strobel_M/0/1/0/all/0/1">Martin Strobel</a>, <a href="http://arxiv.org/find/cs/1/au:+Tong_Y/0/1/0/all/0/1">Yao Tong</a>, <a href="http://arxiv.org/find/cs/1/au:+Narayan_A/0/1/0/all/0/1">Akshay Narayan</a>, <a href="http://arxiv.org/find/cs/1/au:+Soh_H/0/1/0/all/0/1">Harold Soh</a>, <a href="http://arxiv.org/find/cs/1/au:+Doshi_Velez_F/0/1/0/all/0/1">Finale Doshi-Velez</a> Posted: June 23, 2023
There is increasing attention being given to how to regulate AI systems. As
governing bodies grapple with what values to encapsulate into regulation, we
consider the technical half of the question: To what extent can AI experts vet
an AI system for adherence to regulatory requirements? We investigate this
question through two public sector procurement checklists, identifying what we
can do now, what we should be able to do with technical innovation in AI, and
what requirements necessitate a more interdisciplinary approach.
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