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.