Artificial Intelligence in Federal Agencies

The Office of the Chairman of the Administrative Conference is exploring the growing role that artificial intelligence (AI), such as machine learning and related techniques, is playing in federal agency adjudication, rulemaking, and other regulatory activities.

A major component of this initiative consists of a report, including an executive summary and an appendix of use cases, that a team of researchers at Stanford University Law School and New York University (NYU) School of Law delivered to the Office of the Chairman. See David Freeman Engstrom, Daniel E. Ho, Catherine M. Sharkey & Mariano-Florentino Cuéllar, Government by Algorithm: Artificial Intelligence in Federal Administrative Agencies (Feb. 2020) (report to the Admin. Conf. of the U.S.),

The report consists of multiple parts. The first part maps how federal agencies are currently using AI to make and support decisions. A second, related part extends this map by using a sophisticated grasp of AI techniques to highlight promising potential uses of AI in federal agencies. The final part addresses how these uses of AI implicate core administrative law doctrines, such as the nondelegation doctrine, arbitrary-and-capricious review, due process, and rules governing reliance on subordinates for decisions. Professor David Freeman Engstrom of Stanford Law School, Professor Daniel E. Ho of Stanford Law School, Justice Mariano-Florentino Cuéllar of the Supreme Court of California and Stanford Law School, and Professor Catherine M. Sharkey of NYU School of Law serve as principal advisors on this work.

The Office of the Chairman will share with agencies the insights that emerge from this initiative. For a list of other ACUS AI-related initiatives, please see ACUS's AI portal.