Government by Algorithm: Artificial Intelligence in Federal Administrative Agencies

122 Pages Posted: 2 Apr 2020

See all articles by David Freeman Engstrom

David Freeman Engstrom

Stanford Law School

Daniel E. Ho

Stanford Law School

Catherine M. Sharkey

New York University School of Law

Mariano-Florentino Cuéllar

Stanford Law School; Freeman Spogli Institute for International Studies

Date Written: February 1, 2020

Abstract

Artificial intelligence (AI) promises to transform how government agencies do their work. Rapid developments in AI have the potential to reduce the cost of core governance functions, improve the quality of decisions, and unleash the power of administrative data, thereby making government performance more efficient and effective. Agencies that use AI to realize these gains will also confront important questions about the proper design of algorithms and user interfaces, the respective scope of human and machine decision-making, the boundaries between public actions and private contracting, their own capacity to learn over time using AI, and whether the use of AI is even permitted.

These are important issues for public debate and academic inquiry. Yet little is known about how agencies are currently using AI systems beyond a few headline-grabbing examples or surface-level descriptions. Moreover, even amidst growing public and scholarly discussion about how society might regulate government use of AI, little attention has been devoted to how agencies acquire such tools in the first place or oversee their use. In an effort to fill these gaps, the Administrative Conference of the United States (ACUS) commissioned this report from researchers at Stanford University and New York University. The research team included a diverse set of lawyers, law students, computer scientists, and social scientists with the capacity to analyze these cutting-edge issues from technical, legal, and policy angles. The resulting report offers three cuts at federal agency use of AI:

(i) a rigorous canvass of AI use at the 142 most significant federal departments, agencies, and sub-agencies (Part I)

(ii) a series of in-depth but accessible case studies of specific AI applications at eight leading agencies (SEC, CPB, SSA, USPTO, FDA, FCC, CFPB, USPS) covering a range of governance tasks (Part II); and

(iii) a set of cross-cutting analyses of the institutional, legal, and policy challenges raised by agency use of AI (Part III).

Keywords: artificial intelligence, machine learning, administrative agencies, administrative law, algorithmic governance, ACUS report, algorithmic accountability, public administration

Suggested Citation

Engstrom, David Freeman and Ho, Daniel E. and Sharkey, Catherine M. and Cuéllar, Mariano-Florentino, Government by Algorithm: Artificial Intelligence in Federal Administrative Agencies (February 1, 2020). Available at SSRN: https://ssrn.com/abstract=3551505 or http://dx.doi.org/10.2139/ssrn.3551505

David Freeman Engstrom (Contact Author)

Stanford Law School ( email )

559 Nathan Abbott Way
Stanford, CA 94305-8610
United States

Daniel E. Ho

Stanford Law School ( email )

559 Nathan Abbott Way
Stanford, CA 94305-8610
United States
650-723-9560 (Phone)

HOME PAGE: http://dho.stanford.edu

Catherine M. Sharkey

New York University School of Law ( email )

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

Mariano-Florentino Cuéllar

Stanford Law School ( email )

559 Nathan Abbott Way
Stanford, CA 94305-8610
United States
650-723-9216 (Phone)
650-725-0253 (Fax)

Freeman Spogli Institute for International Studies ( email )

United States

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