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REVIEW 4 major objections 5 minor 94 references

One Bad NOFO? AI Governance in Federal Grantmaking

T0 review · 4 major / 5 minor · reviewed 2026-08-15 · deepseek-v4-flash

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Pith's one-line read Federal agencies almost never attach AI-specific conditions to grants, a review of 40,514 notices finds, even when the funded AI touches people's rights.

desk verdict A solid, transparent empirical mapping of AI governance in federal grant notices; the 'only nine' count is real for the corpus but cannot carry the full weight of the authors' general claim, so the paper needs a light revision rather than a rewrite. read the letter →

arxiv 2505.08133 v2 pith:ZXGM5ZMH submitted 2025-05-13 cs.CY cs.AI

Dan Bateyko , Karen Levy This is my paper · ORCID
classification cs.CYcs.AI
keywords AIgovernancefederalgrantsgrantpolicyNoticeofFundingOpportunitydiscretionaryconditionsprocurementanalogyalgorithmicoversight
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper tries to establish that the U.S. federal government's discretionary grantmaking is an overlooked but powerful lever for governing artificial intelligence, and that agencies currently pull that lever almost nowhere. Analyzing more than 40,000 non-defense grant notices posted between 2009 and 2024, the authors find that agencies often promote AI in their grant narratives, encouraging grantees to use the technology, yet only nine grant programs in the entire dataset impose AI-specific review criteria or conditions. That silence persists even in contexts that affect people's rights, such as law enforcement, education, and healthcare, where a comparable federal procurement regime would trigger extra oversight. The paper matters because if true, it means billions of dollars in federal assistance enable AI uses with little pre-award planning or guardrails, leaving agencies unprepared to prevent or correct misuse after the money is out.

What carries the argument

The central object is the Notice of Funding Opportunity (NOFO), the public document through which agencies announce discretionary grants and set program objectives, judging criteria, restrictions, and eligibility. The paper treats NOFOs as a policy instrument: because agencies can rarely add rules after an award begins, whatever conditions appear in the notice are the main pre-award lever for shaping how grantees use AI. The analysis works by collecting NOFO full texts from the federal grants website, searching them for AI keywords derived from OMB guidance, manually reviewing the 633 matches to discard boilerplate references, and inductively coding the remaining 407 opportunities into categories of direct funding, indirect encouragement, rights-impacting contexts, and explicit conditions.

What would settle it

Inspect the grant announcements hosted on the clearinghouses of the major research funders absent from the dataset, or the other roughly 39% of notices that lack full-text attachments, and count how many impose AI-specific review criteria or conditions; if a substantial number do, the 'only nine' finding and the accompanying claim of general silence would weaken.

Watch

Extended reading notes

Core claim

The paper's central discovery is that federal agencies rarely use the conditions available to them in discretionary grants to govern grantees' use of AI. Of the 407 grant opportunities that mention AI in a meaningful way, only nine establish AI-specific review criteria or restrictions, and those are mostly broad disclosure requirements, discouragements, or outright bans rather than domain-tuned oversight. The authors find this silence holds even among grants funding AI in contexts that OMB's own guidance flags as high-impact for civil rights and safety, such as recidivism prediction, student monitoring, and HIV-risk identification. They further show that NOFO narratives promote AI in ways that official spending records and summary listings miss: only 17% of Grants.gov summaries for these AI-related notices contain an AI keyword, and about a third of corresponding spending records do not mention AI at all, suggesting the real federal AI footprint is underreported.

Load-bearing premise

The 40,514 notices with full text attached are representative of all federal discretionary grantmaking, even though attachment coverage is uneven and major research funders that host their own clearinghouses are absent from the dataset.

Editorial extensions

If this is right

  • If the finding holds, federal agencies are currently funding AI adoption on a large scale without corresponding pre-award planning, meaning the HUD surveillance-camera episode is a systemic risk, not an isolated failure.
  • The near-absence of AI-specific conditions implies that agencies are failing to exercise the one control they retain after awards are made, since they rarely add rules mid-award; applicants have little notice of what responsible AI use requires.
  • The underreporting of AI in NOFO summaries and USASpending records suggests that existing measures of federal AI funding, which rely on those records, systematically undercount the true footprint of government-supported AI activity.
  • The paper's comparison with procurement indicates that if agencies began applying procurement-style AI oversight to grants, they would need new capacity, such as AI-literate review panels and monitoring practices, because grant offices are not currently staffed for that role.
  • A shift in administrative policy toward AI conditions, such as a future OMB memo covering grants, could quickly change the observed pattern, making the current silence a contingent policy choice rather than a fixed feature of grantmaking.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Beyond the paper: the same near-silence likely extends to state and local grantmaking, and to other financial assistance instruments like prize competitions, where agencies have even fewer pre-award conditions; a state-level search for AI-specific grant conditions could test this.
  • Beyond the paper: because the dataset underrepresents major research funders that host their own clearinghouses, a fuller count of AI conditions might exceed nine, but the paper's qualitative evidence suggests the increase would be small and unlikely to overturn the claim of general silence.
  • Beyond the paper: the paper's keyword approach could be adapted as a low-cost screening tool for agencies themselves, letting them audit their own NOFOs for AI mentions and conditions before public release, turning the research method into an internal governance practice.
  • Beyond the paper: if agencies adopted disclosure-oriented conditions, modeled on the few examples the paper finds, they would create a public record of what AI uses grantees actually deploy, enabling the transparency that current spending records do not provide.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 5 minor

Summary. This paper examines how U.S. federal agencies govern grantee use of AI through discretionary grant Notices of Funding Opportunity (NOFOs). The authors assemble 40,514 full-text NOFOs from Grants.gov for 2009–2024, keyword-search them using terms from OMB AI guidance, manually review 407 AI-related notices, and classify them as direct or indirect AI funding and as containing or not containing AI-specific review criteria or restrictions. They find that agencies promote AI in program descriptions and review criteria but that only nine opportunities in the dataset impose AI-specific conditions; this pattern is argued to persist even in rights-impacting contexts such as law enforcement, education, and health care. The paper draws lessons from AI procurement scholarship and discusses grant-specific challenges. The authors acknowledge limitations in NOFO coverage and keyword recall.

Significance. If the empirical findings hold up after robustness checks, the paper makes a valuable contribution by identifying federal discretionary grantmaking as an understudied site of AI governance and by introducing a novel, manually reviewed corpus of 40,514 NOFOs. The comparison with procurement governance is apt, and the paper is honest about the major limitations of data coverage and keyword scope. The claim that agencies rarely impose AI-specific conditions, even while promoting AI in program narratives, is a falsifiable and policy-relevant finding. The paper also provides a useful methodological demonstration that Grants.gov full-text notices capture AI-related grant activity that spending summaries and NOFO metadata miss.

major comments (4)
  1. [§3.1, Fig. 1, §3.4, Fig. 3] The funnel in Figure 1 and the coverage table in Figure 3 show that only 40,514 of 66,390 eligible notices (61%) had full-text attachments that could be keyword-searched, with substantial agency-by-year variation and the complete absence of NSF and NIH (Footnote 4). Because the paper's headline claim is the absolute count of nine AI-specific conditions, this missing 39% is load-bearing: if agencies with more mature AI-grant governance are also those that host full notices off Grants.gov, the count could be materially higher. I request a sensitivity analysis that searches at least the Grants.gov summary metadata (and, where feasible, agency-hosted full texts) for the missing records, or that restricts every generalization in the abstract and Section 5 to 'notices in our dataset with full-text attachments.'
  2. [§3.2, Table 1] The keyword list in Table 1, drawn from OMB memos, omits common AI-governance terms such as 'facial recognition,' 'predictive algorithm,' 'automated decision-making,' and 'computer vision,' and the paper itself notes in Section 3.4 that terminology changes over time. Since the count of nine conditions is produced by keyword screening, the authors should validate recall, for example by running an expanded term list on a random sample of the 40,514 full-text NOFOs and reporting how many additional AI-related opportunities and AI-specific conditions are found; without such a check, the near-absence result could reflect search misses rather than agency practice.
  3. [§4.4 vs. Abstract] The abstract and Section 5 claim that the near-absence of AI-specific conditions 'holds even when agencies fund AI uses in contexts affecting people's rights,' but Section 4.4 includes a 2018 NIJ opportunity on AI tools to combat human trafficking that requires AI prototypes to be delivered for third-party auditing. NIJ appears in Section 4.3 as a law-enforcement-context funder, so either this example is itself an AI-specific condition in a rights-impacting context, or the context coding needs clarification; the claim should be reconciled with the enumerated nine.
  4. [§4.4] The manuscript reports that exactly nine opportunities contain AI-specific review criteria or restrictions but provides no table that lists these nine with NOFO identifiers, agencies, years, and coded condition types. Because this count is the central empirical result and Section 4 notes that the boundary between 'conditions' and 'considerations' is contested, a transparent enumeration is necessary for readers to verify the classification and for future work to build on it.
minor comments (5)
  1. [Fig. 1 and §3.1] Figure 1 labels the date filter as '2009–2025' while Section 3.1 states 2009–2024; please align these labels.
  2. [§5.3] The reference list contains a broken citation 'citejegedeChallengeAcceptedCritique2023' in the sentence about Jegede et al.; please fix the citation.
  3. [Table 2] Table 2 lists 'USDOT United States Department of the Treasury'; if this entry is intended to be the Department of the Treasury, the abbreviation is confusing given that DOT already appears, and it should be clarified or corrected.
  4. [§3.3] Section 3.3 does not report whether manual coding was done by one or multiple coders or how disagreements were resolved; adding this procedural detail would strengthen confidence in the counts.
  5. [§3.1] The dataset is described as available 'upon request from the first author'; for a computational social-science contribution, a public repository with code and coded NOFO identifiers would improve reproducibility.

Circularity Check

0 steps flagged · score 1.0 of 10

No material circularity: the paper reports new empirical measurements of public grant documents, with no fitted parameters or derivations that reduce to their inputs.

full rationale

This is an empirical study, not a derivation. The central claims—407 AI-related NOFOs and only nine with AI-specific conditions—are counts produced by keyword searching and manual review of full-text grant attachments. The keyword list is sourced from OMB memoranda (M-25-21 and M-24-10) and used as a measurement instrument; it is not constructed from the outcome being measured, so it does not by construction force the 'only nine' result. The one self-citation (Bateyko et al. 2023, reference [47]) appears in a footnote supporting the historical claim that advocacy organizations urged OMB to apply AI risk rules to grantmaking; that citation is not load-bearing for the empirical findings. The paper itself flags the main validity concern in Section 3.4: 'uploading the full NOFO text is generally optional... our findings understate the total volume of AI-related federal grant activity and may miss certain governance approaches used by agencies absent from our dataset.' That is an acknowledged external-validity limitation about missing documents, not a circular reduction of the result to its inputs. Appendix A further demonstrates independent content by showing that only 17% of Grants.gov summaries and about a third of associated USASpending records contain AI keywords, a discrepancy that would be meaningless if the NOFO counts were merely restatements of the search inputs. No equation, fitted parameter, or self-referential uniqueness theorem is invoked, so the circularity burden is low. The appropriate score is 1, reflecting only a minor, non-load-bearing self-citation.

Assumptions & free parameters 0 free parameters · 3 assumptions · 0 invented entities

The paper's central claim depends on three unverified assumptions: that Grants.gov attachments are representative of all non-defense discretionary NOFOs, that the OMB-derived keyword list captures the AI grant universe, and that the authors' manual coding reliably identifies AI-specific conditions. The first is the most fragile; the paper itself documents substantial gaps in coverage.

assumptions (3)
  • domain assumption NOFO full-text attachments available on Grants.gov are a representative source for detecting agency-set AI conditions.
    Only 40,514 of 66,390 non-defense discretionary notices (61%) have matched attachments; agencies like NSF and NIH post elsewhere. If the missing notices contain more AI conditions, the 'only nine' finding would not generalize. The authors acknowledge understatement in Section 3.4 but still state the headline count without this qualifier.
  • domain assumption The OMB-derived AI keyword list captures the relevant universe of AI-related grant opportunities.
    Terms come from M-25-21 and M-24-10 (Table 1); the authors note in Section 3.4 that other definitions and older terms like 'big data' are excluded, so some AI-related grants may be missed.
  • domain assumption Manual inductive coding reliably identifies AI-specific conditions and direct versus indirect funding.
    No inter-coder reliability or second-coder check is reported; all counts (9 conditions, 407 AI-related opportunities, 25 rights-impacting contexts) depend on the authors' reading of the harvested paragraphs.

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Cite this review

Pith. "Pith review of One Bad NOFO? AI Governance in Federal Grantmaking." pith.science (2026). https://pith.science/paper/ZXGM5ZMH

@misc{pith2026250508133,
  author       = {Pith},
  title        = {Pith review of: One Bad NOFO? AI Governance in Federal Grantmaking},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/ZXGM5ZMH}},
  note         = {Machine review of arXiv:2505.08133}
}
read the original 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

Figures reproduced from arXiv: 2505.08133 by the authors.

Figure 1
Figure 1. A funnel chart showing the progressive filtering [PITH_FULL_IMAGE:figures/full_fig_p004_1.png] view at source ↗
Figure 2
Figure 2. AI-related notice of funding opportunity trends [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 3
Figure 3. The above heatmap shows our Notice of Funding Opportunity collection from Grants.gov across federal agencies and [PITH_FULL_IMAGE:figures/full_fig_p013_3.png] view at source ↗

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