Reports  |    |  February 18, 2020

Government by Algorithm: Artificial Intelligence in Federal Administrative Agencies

Report commissioned by the Administrative Conference of the United States, an agency that provides advice for all federal regulatory agencies. Explores the topic of AI’s growing role in federal agencies. Written by David Freeman Engstrom, Daniel E. Ho, Catherine M. Sharkey, Mariano-Florentino Cuéllar. 122 pages.

Executive Summary:

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 headlinegrabbing 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:

  • a rigorous canvass of AI use at the 142 most significant federal departments, agencies, and sub-agencies (Part I)
  • a series of in-depth but accessible case studies of specific AI applications at seven leading agencies covering a range of governance tasks (Part II);
  • and a set of cross-cutting analyses of the institutional, legal, and policy challenges raised by agency use of AI (Part III).

Taken together, these analyses yield five main findings. First, the government’s AI toolkit is diverse and spans the federal administrative state. Nearly half of the federal agencies studied (45%) have experimented with AI and related machine learning (ML) tools. Moreover, AI tools are already improving agency operations across the full range of governance tasks, including:

  • Enforcing regulatory mandates centered on market efficiency, workplace safety, health care, and environmental protection;
  • Adjudicating government benefits and privileges, from disability benefits to intellectual property rights;
  • Monitoring and analyzing risks to public health and safety;
  • Extracting useable information from the government’s massive data streams, from consumer complaints to weather patterns;
  • and Communicating with the public about its rights and obligations as welfare beneficiaries, taxpayers, asylum seekers, and business owners.

The government’s AI toolkit spans the full technical scope of AI techniques, from conventional machine learning to more advanced “deep learning” with natural language and image data. [ . . . ]