Regulating Multifunctionality

Cary Coglianese; Colton R. Crum

REVIEW 3 major objections 5 minor 63 references

Regulating Multifunctionality

T0 review · 3 major / 5 minor · reviewed 2026-08-10 · deepseek-v4-flash

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Pith's one-line read The paper argues that regulating multifunctional AI calls for management-based regulation: mandated, audited internal risk-management plans rather than fixed rules.

desk verdict A coherent policy synthesis whose management-based recommendation inherits the very information problem it uses to reject alternatives; send to referees, but the auditability premise must be defended. read the letter →

arxiv 2502.15715 v1 pith:VOVQONF6 submitted 2025-01-26 cs.CY cs.AI

Cary Coglianese , Colton R. Crum This is my paper · ORCID

classification cs.CYcs.AI

keywords artificialintelligencefoundationmodelsgenerativeAIregulationmanagement-basedheterogeneityperformancestandardsexpostliability

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 argues that foundation models and generative AI are too multifunctional for prescriptive, one-size-fits-all regulation, because neither regulators nor developers can anticipate all the uses a general model will be put to. It claims that even flexible tools like performance standards and ex post liability face monitoring, causation, and foresight problems that make them unlikely to carry the regulatory burden alone. The recommended alternative is management-based regulation, under which developers must maintain and update internal risk-management plans, subject to auditing, with regulators staying vigilant and agile. For a sympathetic reader, the payoff would be a regulatory strategy that can govern AI without pretending to predict every use.

What carries the argument

The central mechanism is management-based regulation, defined as a regulatory strategy that requires a regulated entity to develop and implement an internal plan for identifying and monitoring risks, establishing protective procedures, and documenting changes over time, with regulators auditing plans and their execution. It is carried by the paper's typology of AI heterogeneity—design heterogeneity, use heterogeneity, and problem heterogeneity—which is used to show why prescriptive rules, performance standards, and liability each fail at some step of specifying or verifying AI conduct, while an audited internal planning process does not need that specification.

What would settle it

A regulator would need to show a concrete counterexample: a general-purpose foundation model for which a measurable safety outcome (for example, a bounded rate of harmful outputs under adversarial prompting) was specified in advance and enforced, or a jurisdiction where audited management-based plans were demonstrably implemented without privileged access to model internals. Either observation would test the paper's central dichotomy.

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Extended reading notes

Core claim

The paper's central claim is that management-based regulation is arguably the only regulatory strategy equipped to handle the heterogeneity challenges posed by foundational and generative AI. Because a foundation model is a Swiss-army-knife technology whose uses are limited only by users' imagination, regulation cannot specify means or measurable outcomes in advance. Management-based regulation adapts by obligating each developer to build an internal, documented process for identifying, monitoring, and mitigating risks, and by having regulators audit that process and its implementation over time. If correct, this means the central task of AI governance shifts from writing technology rules to building institutional capacity for ongoing oversight of firms' internal risk management.

Load-bearing premise

The argument depends on regulators being able to audit developers' internal risk-management plans and their implementation, even though the paper does not explain how regulators obtain the information and monitoring capacity that it says is missing when it rejects performance standards.

Editorial extensions

If this is right

Regulators of AI should focus less on dictating model designs or banning outputs and more on requiring developers to maintain audited risk-management plans.
Performance standards may remain workable for narrow, single-function deployments of AI, but not for general-purpose models with open-ended uses.
Ex post liability can supplement, but not replace, proactive oversight, because it acts only after harm and faces causation and foreseeability problems.
Information disclosure, such as model cards, can support management-based regulation but needs to be designed so ordinary users can act on it.
AI governance will depend on regulatory resources, human talent, and organizational culture, not just on the choice of legal instruments.

Reading between the lines

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

If management-based regulation is right, then the most consequential design question for AI law is auditability: what records, metrics, and access rights make an internal AI risk-management plan genuinely verifiable by outsiders.
The same logic may extend to open-weight models, where the developer's internal plan cannot control downstream users; management-based obligations might need to move to deployers or hosting platforms, a step the paper only gestures at.
The argument implies a testable prediction: jurisdictions that adopt audited management-based requirements should show fewer severe AI incidents per deployment than those relying on voluntary guidance or pure liability.
The paper's analogy to the Swiss army knife suggests a research program: map which categories of AI uses have stable, measurable risk endpoints, because those are the islands where performance standards can survive.

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Editorial analysis

A structured set of objections, weighed in public.

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

Desk Editor's Note

read the letter

Here is my take. This is a clear, well-organized policy synthesis, not a research paper. The useful contribution is the multifunctionality framing: sorting AI into single-function versus multifunctional tools, and separating design heterogeneity, use heterogeneity, and problem heterogeneity. That framing does real work in explaining why prescriptive rules cannot govern foundation models. The chapter is also honest that performance standards and liability have their own limits, and it does not oversell them.

The soft spot is exactly where the reader and the stress-test put it. Section 5.4 asserts regulators can subject required plans to routine auditing, but it never says what an audit would inspect or how anyone would judge whether an internal risk-management plan is adequate for a model whose downstream uses are unknown. The information and monitoring deficits the chapter uses in Section 5.1 to reject performance standards apply directly to this claim. A firm can produce thick red-teaming documentation and a clean plan-do-check-act cycle while missing the emergent risk that actually materializes. Unless the chapter can point to observable, auditable intermediate states, or to auditing methods that work on opaque models, the central recommendation rests on an assumption, not an argument.

The other weakness is evidentiary. The chapter says management-based regulation has proven effective in other contexts of heterogeneity, but the citations for that claim are mostly the authors' own prior work. Self-citation is not disqualifying—Coglianese is the scholar who developed this approach—but independent empirical support from food safety, environmental, or process-safety domains would be doing real work here, and it is not supplied. Nor does the chapter engage the AI auditing literature that might make the auditability premise concrete.

Do not read this as a flawed empirical claim; read it as a position piece. As a handbook chapter, it is a serviceable statement of the management-based regulation position, and the first half is genuinely useful for teaching and framing. The central argument is coherent even if the auditability premise is unresolved. I would send it to referees: a good referee will push exactly the right questions, and the authors have the expertise to respond. As it stands, it is publishable with revision, but only if the auditability gap is addressed rather than asserted.

Referee Report

3 major / 5 minor

Summary. This policy chapter argues that foundation models and generative AI exhibit an extreme form of heterogeneity—across designs, uses, and risks—that makes prescriptive ('micro-means') regulation infeasible. The authors review four more flexible regulatory options—performance standards, information disclosure, ex post liability, and management-based regulation—and conclude that management-based regulation, which would require AI developers to maintain and periodically update internal risk-management plans subject to auditing, is the most realistic primary approach. They add that regulatory vigilance, agility, resources, and human capital are necessary complements. The chapter is a conceptual and argumentative contribution, not an empirical study.

Significance. If accepted, the chapter would redirect AI governance discussions away from model-specific rules and toward organizational process regulation, a position with real policy consequences. The paper offers a useful typology of AI heterogeneity and a balanced account of the limits of prescriptive rules, performance standards, and liability. Its central positive claim, however, rests on an unexamined auditability premise and on effectiveness evidence drawn substantially from the authors' own prior work in other regulatory domains. The chapter is therefore a valuable framing contribution, but it does not yet establish that management-based regulation can succeed for multifunctional AI.

major comments (3)

[Abstract and §5.4, first paragraph] The claim that management-based regulation 'has proven effective in other contexts of heterogeneity' and is 'arguably the only regulatory strategy equipped' to handle foundational AI is supported only by citations to [13], [20], [11], and forthcoming [16], all authored or co-authored by the first author. No empirical study of management-based regulation applied to AI is cited, and no mechanism is given that would explain why the industrial-safety, environmental, and food-safety record transfers to foundation models. This is a load-bearing premise for the chapter's central recommendation; the authors should either supply independent evidence or explicitly qualify the claim as an analogy rather than a demonstrated result.
[§5.4, fourth paragraph, and §5.1, last paragraph] The chapter rejects performance standards partly because regulators cannot specify or measure the risks posed by multifunctional models, stating that such limitations may make performance standards 'as infeasible as reliance on prescriptive or micro-means regulations.' Management-based regulation, however, requires regulators or third-party auditors to judge whether an internal plan is adequate—which risks it should cover, what monitoring is sufficient, and whether implementation is genuine. The paper does not explain what an audit would inspect or how an auditor can distinguish substantive risk management from paper compliance without access to training data, deployment telemetry, or model internals. The same information and monitoring deficits therefore apply to the auditability of management-based plans; this should be addressed by specifying audit criteria, information rights, or a role for external model evaluations.
[§6 (Regulatory Vigilance and Agility)] The recommendation assumes regulators will have the capacity to audit AI firms' internal processes, but the chapter's own Section 6 acknowledges only the need for 'resources, financial resources, technological tools, and human capital' without explaining how these can be obtained or whether existing agencies are capable of exercising them. Given the heterogeneity and pace the paper describes, this capacity assumption is not trivial. The authors should offer concrete institutional design suggestions—such as specialized agencies, certification schemes, mandatory data-access regimes, or sunset-and-review mechanisms—or acknowledge that the proposal is conditional on an as-yet-unmet regulatory capability.

minor comments (5)

[§3, paragraph on the Zelenskyy video] The name 'President V olodymyr Zelenskyy' contains a stray space after the initial; it should read 'President Volodymyr Zelenskyy.'
[§5.2, subsection on performance disclosure] The phrase 'model or system cards' should be 'model cards or system cards' for parallel phrasing and clarity.
[§5.4, paragraph on the iterative process] The 'plan-do-check-act' cycle is invoked without citation or definition; since it is a specific management doctrine, provide a reference or a one-sentence explanation of what each step requires.
[§6, opening paragraph] The term 'regulatory excellence' is used as if self-explanatory; tie it to the Best-in-Class Regulator Initiative cited at [41] or define its components explicitly.
[§7, Conclusion] The closing sentence states that 'meaningful, multifaceted regulatory strategies do exist,' but the body of the chapter only argues that management-based regulation is promising while performance standards and liability have serious limitations. The conclusion should be softened to match the evidence presented.

Circularity Check

1 steps flagged · score 4.0 of 10

Policy recommendation leans on self-cited effectiveness claim; core heterogeneity analysis is otherwise independent.

self citation load bearing [Section 5.4, Management-Based Regulation]
"Management-based regulation is arguably the only regulatory strategy equipped to handle the heterogeneity challenges posed by foundational and generative AI. This type of regulation is already used widely in other diverse and dynamic risk settings where neither prescriptive nor performance standards are feasible [13, 20, 11]."

The paper's central recommendation—that management-based regulation is the only strategy equipped for multifunctional AI—is supported by citations to the authors' own prior work: [13] Coglianese, [20] Coglianese and Lazer, and [11] Coglianese. The abstract's stronger claim that this approach 'has proven effective in other contexts of heterogeneity' rests on this same self-cited lineage. No independent empirical studies or external benchmark are offered for the effectiveness premise. The argument therefore partly reduces to the authors' own prior assertions, even though the surrounding analysis of AI heterogeneity and the critiques of prescriptive, performance, and liability approaches are independently developed.

full rationale

This is a policy-analysis paper, not a formal derivation, so there are no equations, fitted parameters, or construction-level reductions. The main circular element is the empirical grounding for the central recommendation: Section 5.4 asserts that management-based regulation is 'arguably the only regulatory strategy equipped' for multifunctional AI and supports this with three citations, all of which include the present first author. The abstract likewise states that the approach 'has proven effective in other contexts of heterogeneity' without independent support. That self-citation is load-bearing because the effectiveness of management-based regulation is the key premise for choosing it over the alternatives. However, much of the paper's content—the taxonomy of heterogeneity, the analysis of why prescriptive rules, performance standards, disclosure, and liability face challenges, and the emphasis on regulatory vigilance—is developed from general reasoning and external examples. The auditability premise in Section 5.4 is arguably an unsupported assumption and a possible inconsistency with Section 5.1, but that is a correctness or completeness concern rather than a circularity. Overall, the central claim retains independent analytical content, but its pivotal empirical premise is substantially self-referential, warranting a moderate score of 4.

Assumptions & free parameters 0 free parameters · 4 assumptions · 0 invented entities

The paper is an argumentative policy analysis, so it introduces no fitted parameters and no invented entities. The argument rests on domain assumptions about the limits of prescriptive and performance regulation and about the feasibility of auditing management plans; it also assumes the effectiveness of management-based regulation transfers from other sectors, an assumption supported mainly by the authors' own prior work.

assumptions (4)

domain assumption Regulators cannot foresee the full range of uses of foundation models, making prescriptive micro-means regulation infeasible.
Stated in Sections 2 and 4; this premise underlies the rejection of prescriptive rules. It is plausible but is asserted rather than empirically established.
domain assumption Performance standards require a defined problem and a measurable compliance test, which is unavailable for multifunctional AI.
Section 5.1 argues performance standards will be infeasible because not all problems can be specified and monitoring is difficult. This treats AI evaluation as too immature to support such standards.
ad hoc to paper Management-based regulation has proven effective in other heterogeneous contexts and will be effective for AI.
Section 5.4 states this and cites refs [13], [20], and [11], which include the authors' own prior scholarship. No direct evidence from AI oversight is presented.
domain assumption Regulators can audit the internal risk-management plans of AI developers.
Section 5.4 asserts regulators can conduct routine and emergency audits, but the paper does not specify how they will acquire the needed expertise or information. This is load-bearing because the recommendation depends on meaningful oversight.

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Cite this review

Pith. "Pith review of Regulating Multifunctionality." pith.science (2026). https://pith.science/paper/VOVQONF6

@misc{pith2026250215715,
  author       = {Pith},
  title        = {Pith review of: Regulating Multifunctionality},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/VOVQONF6}},
  note         = {Machine review of arXiv:2502.15715}
}

read the original abstract

Foundation models and generative artificial intelligence (AI) exacerbate a core regulatory challenge associated with AI: its heterogeneity. By their very nature, foundation models and generative AI can perform multiple functions for their users, thus presenting a vast array of different risks. This multifunctionality means that prescriptive, one-size-fits-all regulation will not be a viable option. Even performance standards and ex post liability - regulatory approaches that usually afford flexibility - are unlikely to be strong candidates for responding to multifunctional AI's risks, given challenges in monitoring and enforcement. Regulators will do well instead to promote proactive risk management on the part of developers and users by using management-based regulation, an approach that has proven effective in other contexts of heterogeneity. Regulators will also need to maintain ongoing vigilance and agility. More than in other contexts, regulators of multifunctional AI will need sufficient resources, top human talent and leadership, and organizational cultures committed to regulatory excellence.

Discussion (0). Continue with ORCID to comment.

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https://www.noemamag.com/the-danger-of-superhuman-ai-is-not- what-you-think/

Pith tools

Reviewed August 10, 2026 · model on record in the stance chip above.

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