One Bad NOFO? AI Governance in Federal Grantmaking
Open accessAbstract
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 Grants.gov 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.
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The potential of public procurement as an AI governance mechanism is currently underutilised in practice.
“Despite the promise of public procurement to act as an AI governance mechanism, first insights suggest that this potential is currently underutilised in practice [13, 129, 144, 160].”
From “It’s Just a Wild, Wild West”: Harnessing Public Procurement as an AI Governance Mechanism (2026) · cites this paper as [13]
Section 5.1 describes procurement contracts as a way to tailor oversight of AI deployments and notes practical challenges for procurement officials, including monitoring deployments. But the paper’s finding that agencies rarely use conditions to govern AI concerns federal grants, not procurement; it does not establish that public procurement’s governance potential is generally underutilised in practice. The indexed passages provide no page numbers. (LLM, conf=0.82)
Experimental — accuracy not yet measuredThe contractual terms and requirements central to procurement processes directly shape and govern the design of the procured system.
“Moreover, the contractual terms and requirements that are central to procurement processes directly shape and govern the design of the procured system, further highlighting the relevance of public procurement for this research domain [9, 13, 95].”
From “It’s Just a Wild, Wild West”: Harnessing Public Procurement as an AI Governance Mechanism (2026) · cites this paper as [13]
Section 5.1, “Lessons from AI Procurement Literature” (page unavailable), says contracting lets officials tailor restrictions to deployed AI tools and that procurement officials make policy choices about their purchase and use. It does not say that contractual requirements directly shape the procured system’s design. (LLM, conf=0.81)
Experimental — accuracy not yet measuredPublic procurement could position the public sector as a gatekeeper, ensuring AI systems purchased by government agencies are better aligned with the public interest.
“According to these works, public procurement could position the public sector as a gatekeeper, ensuring that AI systems purchased by government agencies (where such systems typically have direct societal impact) are better aligned with the public interest [13, 94].”
From “It’s Just a Wild, Wild West”: Harnessing Public Procurement as an AI Governance Mechanism (2026) · cites this paper as [13]
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Cites
The Fallacy of AI Functionality
Raji, I. D., Kumar, I. E., Horowitz, A., & Selbst, A. (2022) · 2022 ACM Conference on Fairness Accountability and Transparency · doi:10.1145/3531146.3533158 · Cited by 251 · see citing works
Defining AI in Policy versus Practice
Krafft, P. M., Young, M., Katell, M., Huang, K., & Bugingo, G. (2020) · Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society · doi:10.1145/3375627.3375835 · Cited by 129 · see citing works
Procurement As Policy: Administrative Process for Machine Learning
Mulligan, D. K., & Bamberger, K. A. (2019) · SSRN Electronic Journal · doi:10.2139/ssrn.3464203 · Cited by 75 · cited 2× · see citing works
“Mulligan and Bamberger [64] propose that procurement officials make policy choices about the purchase and use of machine learning tools through procurement processes.”
Flagged first, then by citations
Research works printed but not linked: 24
- Bettina K. Arkhurst and Wyatt Green Williams. 2024. Realizing Justice40: Ad- dressing Structural Funding Barriers for Equitable Community Engagement in Energy RD&D. Journal of Science Policy & Governance 23, 02 (March 2024). https://doi.org/10.38126/JSPG230201 · DOI
- Pierre Azoulay and Danielle Li. 2020. Scientific Grant Funding. Technical Report. National Bureau of Economic Research, Cambridge, MA. https://doi.org/10.3386/ w26889 · DOI
- Barry Friedman, Rachel Harmon, and Farhang Heydari. 2023. The Fed- eral Government's Role in Local Policing. Virginia Law Review 109, 8 (2023). https://virginialawreview.org/articles/the-federal-governments-role- in-local-policing/
- Christopher R. Berry, Barry C. Burden, and William G. Howell. 2010. The Presi- dent and the Distribution of Federal Spending. The American Political Science Re- view 104, 4 (2010), 783-799. jstor:40982897 https://www.jstor.org/stable/40982897
- Bruce Allen Brown. 1988. A Federal Agency's Authority to Use a Cooperative Agreement for a Particular Project. Public Contract Law Journal 18, 1 (1988). https://www.jstor.org/stable/25755554
- Cary Coglianese. 2023. Procurement and Artificial Intelligence. https://doi.org/ 10.2139/ssrn.4591724 · DOI · Paper page
- Catherine Crump. 2016. Surveillance Policy Making by Procurement. Washington Law Review 91, 4 (2016). https://doi.org/10.2139/ssrn.2737006 · DOI
- Philip Hamburger. 2021. Purchasing Submission: Conditions, Power, and Freedom. Harvard University Press, Cambridge, Massachusetts.
- Peter Henderson, Ben Chugg, Brandon Anderson, and Daniel E. Ho. 2022. Be- yond Ads: Sequential Decision-Making Algorithms in Law and Public Policy. In Proceedings of the 2022 Symposium on Computer Science and Law (CSLAW '22). Association for Computing Machinery, New York, NY, USA, 87-100. https: //doi.org/10.1145/3511265.3550439 · DOI
- Christie Lawrence, Isaac Cui, and Daniel Ho. 2023. The Bureaucratic Challenge to AI Governance: An Empirical Assessment of Implementation at U.S. Federal Agencies. In Proceedings of the 2023 AAAI/ACM Conference on AI, Ethics, and Society (AIES '23). Association for Computing Machinery, New York, NY, USA, 606-652. https://doi.org/10.1145/3600211.3604701 · DOI
- Miranda J. Lee-Easton, Stephen Magura, and Michael J. Maranda. 2022. Uti- lization of Evidence-Based Intervention Criteria in U.S. Federal Grant Funding Announcements for Behavioral Healthcare. INQUIRY: The Journal of Health Care Organization, Provision, and Financing 59 (Jan. 2022), 00469580221126295. https://doi.org/10.1177/00469580221126295 · DOI
- Karen Levy, Kyla E. Chasalow, and Sarah Riley. 2021. Algorithms and Decision- Making in the Public Sector. Annual Review of Law and Social Science 17, Volume 17, 2021 (Oct. 2021), 309-334. https://doi.org/10.1146/annurev-lawsocsci-041221- 023808 · DOI
- Thomas O McGarity. 1993. Peer Review in Awarding Discretionary Grants in the Arts and Sciences. Administrative Conference of the United States (1993).
- Neil Millar, Bojan Batalo, and Brian Budgell. 2022. Trends in the Use of Promo- tional Language (Hype) in National Institutes of Health Funding Opportunity Announcements, 1992-2020. JAMA Network Open 5, 11 (Nov. 2022), e2243221. https://doi.org/10.1001/jamanetworkopen.2022.43221 · DOI
- James F. Nagle. 1994. Review of Essentials of Grant Law Practice. Public Contract Law Journal 23, 2 (1994), 301-303. jstor:25754134 https://www.jstor.org/stable/ 25754134
- Arvind Narayanan and Sayash Kapoor. 2024. AI Snake Oil: What Artificial Intelli- gence Can Do, What It Can't, and How to Tell the Difference. Princeton University Press, Princeton.
- Administrative Conference of the United States. 1982. Resolving Disputes Under Federal Grant Programs. https://www.acus.gov/recommendation/resolving- disputes-under-federal-grant-programs
- Administrative Conference of the United States. 2023. Peer Review in the Award of Discretionary Grants. https://www.acus.gov/document/peer-review-award- discretionary-grants
- Eloise Pasachoff. 2014. Agency Enforcement of Spending Clause Statutes: A Defense of the Funding Cut-Off. Yale Law Journal 124 (Nov. 2014). https://www. yalelawjournal.org/article/agency-enforcement-of-spending-clause-statutes
- Eloise Pasachoff. 2020. Federal Grant Rules and Realities in the Intergovernmental Administrative State: Compliance, Performance, and Politics. Yale Journal on Regulation 37 (2020).
- Eloise Pasachoff. 2022. Executive Branch Control of Federal Grants: Policy, Pork, and Punishment. Ohio State Law Journal 83, 6 (May 2022). https://papers.ssrn. com/abstract=4107983
- W. Nicholson Price. 2019. Grants. Berkeley Technology Law Journal 34, 1 (2019), 1-66. https://www.jstor.org/stable/26755225
- KM Rylander. 1998. "Scanwell" Plus: Challenging the Propriety of a Federal Agency's Decision to Use a Federal Grant and Cooperative Agreement. Public Contract Law Journal 28, 1 (1998), 69-87.
- Jess Whittlestone and Jack Clark. 2021. Why and How Governments Should Monitor AI Development. arXiv:2108.12427 [cs] http://arxiv.org/abs/2108.12427 · arXiv
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“It’s Just a Wild, Wild West”: Harnessing Public Procurement as an AI Governance Mechanism
Hudig, A. I., Kallina, E., & Singh, J. (2026) · Proceedings of the 2026 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3791968 · Cited by 3 · see citing works
“…the requirements by which federal financial assistance is awarded to federal agencies in the United States, similar to tender requirements or RFPs [13]. pattern in the UK, i.e. that governments often fail to act as “intelli-gent clients” , neglecting to use contractual levers to meet strategic…”
Influential
- Authors
- Bateyko, D., & Levy, K.
- Affiliations
- Cornell University (US)
- Venue
- Proceedings of the 2025 ACM Conference on Fairness, Accountability, and Transparency
- Publisher
- ACM
- Published
- 2025-06-23
- Pages
- 1640-1652
- OpenAlex
- W4411541885
- Cited by
- 3
- References
- 3
- Citations by year
- 2026: 3
- FWCI
- 3.05
- Type
- conference paper
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- ACM Conference on Fairness, Accountability, and Transparency (FAccT) (publishedVersion)
- arXiv (Cornell University) (submittedVersion, cc-by)
- arXiv (Cornell University) (cc-by)
- Funders
- John D. and Catherine T. MacArthur Foundation
- Topics
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Social Sciences › Law › Digitalization, Law, and Regulation
- Social Sciences › Political Science and International Relations › E-Government and Public Services
- Keywords
- AI governance, discretionary grants, federal agencies, AI policy, AI procurement, transparency and accountability, privacy preservation
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