AI for Retrospective Review

36 Pages Posted: 24 Sep 2021 Last revised: 6 Dec 2021

Date Written: June 1, 2021

Abstract

This article explores the significant administrative law issues that agencies will face as they devise and implement AI-enhanced strategies to identify rules that should be subject to retrospective review. Against the backdrop of a detailed examination of HHS’s “AI for Deregulation” pilot and the very first use of AI-driven technologies in a published federal rule, the article proposes enhanced public participation and notice-and-comment processes as necessary features of AI-driven retrospective review. It challenges conventional wisdom that divides uses of AI technologies into those that “support” agency action—and therefore do not implicate the APA’s directives—and those that “determine” agency actions and thus should be subject to the full panoply of APA demands. In so doing, it takes aim at the talismanic significance of “human in the loop” that shields AI uses from disclosure and review by casting them in a merely supportive role.

Keywords: AI, artificial intelligence, human in the loop, retrospective

JEL Classification: K23

Suggested Citation

Sharkey, Catherine M., AI for Retrospective Review (June 1, 2021). 8 Belmont Law Review 374 (2021), NYU School of Law, Public Law Research Paper No. 21-46, Available at SSRN: https://ssrn.com/abstract=3927987

Catherine M. Sharkey (Contact Author)

New York University School of Law ( email )

40 Washington Square South
New York, NY 10012-1099
United States
212-998-6729 (Phone)

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