One Bad NOFO? AI Governance in Federal Grantmaking

TL;DR

The paper investigates how federal discretionary grantmaking serves as an AI governance mechanism by analyzing Notices of Funding Opportunity (NOFOs) to see how agencies set AI priorities and restrict AI use. Using a novel dataset of 40,514 NOFOs from Grants.gov (2009–2024) and qualitative coding, it identifies 407 AI-related opportunities, with only nine providing explicit AI-specific review criteria or restrictions. The findings show that while agencies increasingly promote AI in grant narratives, formal pre-award AI governance is scarce, though some high-risk contexts reveal guardrails. The study draws lessons from AI procurement scholarship to argue for more transparent, accountable, and risk-aware grant policy, and highlights the need for further research on how grantmaking interfaces with broader AI governance goals.

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.

Figures (3)

• Figure 1: A funnel chart showing the progressive filtering of notices in the Grants.gov database. The chart begins with 80,246 total opportunities and narrows down through stages such as removing dummy records, applying filters for discretionary grants and date ranges, excluding defense and intelligence agencies, and identifying AI-related opportunities through manual review, resulting in 407 AI-related notices.
• Figure 2: AI-related notice of funding opportunity trends by agency over time. We see a growing number of agencies that previously had not shown interest in AI prioritizing its research and use in their grant announcements starting from 2018. The full agency names matching these abbreviations are available in Appendix \ref{['fig:agencynames']}.
• Figure 3: The above heatmap shows our Notice of Funding Opportunity collection from Grants.gov across federal agencies and years. Each cell displays both raw counts (attachments/total notices) and percentages. Cell colors indicate the percentage of notices with successfully collected attachments, ranging from light to dark blue. Light grey cells indicate years with no grant notices for that agency.