One Bad NOFO? AI Governance in Federal Grantmaking

TL;DR

This paper investigates how U.S. federal agencies influence AI development and use through grant policies, revealing a largely overlooked governance role that shapes AI adoption via funding criteria and restrictions.

Contribution

It introduces a novel dataset of over 40,000 federal grant notices and analyzes how agencies regulate AI use in grantmaking, highlighting gaps in transparency and oversight.

Findings

Agencies promote AI in grant notices but rarely specify AI-related judging criteria.

Few grant notices include restrictions or oversight mechanisms for AI use.

Grant notices serve as an emerging site of AI policymaking, often out of step with other regulations.

Abstract

Much scholarship considers how U.S. federal agencies govern artificial intelligence (AI) through rulemaking and their own internal use policies. But agencies have an overlooked AI governance role: setting discretionary grant policy when directing billions of dollars in federal financial assistance. These dollars enable state and local entities to study, create, and use AI. This funding not only goes to dedicated AI programs, but also to grantees using AI in the course of meeting their routine grant objectives. As discretionary grantmakers, agencies guide and restrict what grant winners do -- a hidden lever for AI governance. Agencies pull this lever by setting program objectives, judging criteria, and restrictions for AI use. Using a novel dataset of over 40,000 non-defense federal grant notices of funding opportunity (NOFOs) posted to the U.S. federal grants website between 2009 and 2024, we analyze how agencies regulate the use of AI by grantees. We select records mentioning AI and review their stated goals and requirements. We find agencies promoting AI in notice narratives, shaping adoption in ways other records of grant policy might fail to capture. Of the grant opportunities that mention AI, we find only a handful of AI-specific judging criteria or restrictions. This silence holds even when agencies fund AI uses in contexts affecting people's rights and which, under an analogous federal procurement regime, would result in extra oversight. These findings recast grant notices as a site of AI policymaking -- albeit one that is developing out of step with other regulatory efforts and incomplete in its consideration of transparency, accountability, and privacy protections. The paper concludes by drawing lessons from AI procurement scholarship, while identifying distinct challenges in grantmaking that invite further study.