Tomer Jordi Chaffer (McGill U Law) has posted “On the Institutional Origins of the Agentic Web” (Harvard Journal of Law and Technology Digest, Forthcoming) on SSRN. Here is the abstract:

Artificial intelligence (AI) agents are emerging as autonomous delegates that act, decide, and transact on users’ behalf across digital environments. Their rise marks a turning point for internet governance: will authority over these agents be defined by proprietary platform rules or by open protocols that enable portable identity, verifiable delegation, and accountable behavior? The recent Amazon–Perplexity dispute illustrates this institutional crossroads. If platforms prevail, agentic action will remain confined within walled gardens; if protocols do, authority may shift toward interoperable infrastructures that allow agents to act as true extensions of the user. Ultimately, the question is not whether the agentic web will be governed—but who will govern it, and on what terms. This commentary situates that question within a broader exploration of institutional design, protocol governance, and the emerging duty of care that will define accountability in an era of autonomous systems.

Rajat Pandey (MindscopeAi LLP) has posted “The Agentic AI Governance Framework: A Universal Model for Risk, Accountability, and Compliance in Autonomous Systems” (https://orcid.org/0009-0001-8440-2191) on SSRN. Here is the abstract:

The rapid and enthusiastic interest in development and deployment of Agentic AI and especially autonomous agents which are capable of independent reasoning, leveraging and  executing tools, has outpaced the definition of governance mechanism that is required to manage their operational, ethical and regulatory risks.There are existing AI governance models like NIST AI RMF, ISO/IEC 42001, do provide foundational controls however, they are very generic and lack the implementation depth for these conitnuously acting, multi-agent systems.

This paper introduces The Agentic AI Governance Framework, which is a six principled lifecycle model designed to ensure traceability, accountability, and regulatory alignment across agentic deployments. The framework formalizes quantifiable controls through two applied mechanisms: the Agentic Log Retention Index (ALRI), which defines evidenceretention periods based on agent risk and jurisdiction, and a Runtime Governance Architecture integrating human-in-loop escalation, tool-access allow-lists, and modelprovenance capture. Implementation mappings are provided for major orchestration environments including LangChain, Power Platform, and UiPath. 

Adoption of the framework enables organizations to reconstruct agentic decisions within 24 hours, achieve ≥ 95 % provenance coverage, and detect behavioral drift within 10 % variance. The proposed model bridges the current gap between compliance principles and deployable governance practice, establishing a reusable baseline for safe and auditable Agentic AI operations.

Wulf A. Kaal (U St. Thomas Law (Minnesota)) has posted “From Neoclassical to Computative Labor: Foundations for a Testable Theory of Reputation Governance in the Agent Economy” on SSRN. Here is the abstract:

Artificial agents are becoming economic actors, performing open-ended cognitive work at a marginal cost that approaches zero. Neoclassical economics, built on scarce human labor allocated by price, does not describe this regime. This paper sets out the foundations of an alternative and argues that the alternative is empirically testable now. We distinguish the neoclassical labor force (NCLF) and neoclassical labor market (NCLM) from their computative counterparts, the computative labor force (CELF) and computative labor market (CELM), in which the binding coordination constraint is accumulated reputation rather than scarcity-driven price. From a synthesis of Arrow’s impossibility theorem, the Folk Theorems of repeated games, and incomplete-contract theory, we restate a foundational result: any fixed governance rule set is eventually dominated, so coordination among autonomous agents requires institutions that govern their own evolution, with reputation as the operative signal. We then advance the methodological claim that motivates the paper. The distinction between neoclassical and computative labor is studiable today, because capable agents, on-chain coordination substrates, and reputation primitives already exist, and the competing predictions of the two accounts are falsifiable in controlled multi-agent settings. We situate the argument within a sixpaper research arc that substantiates these foundations, and we state the propositions the arc evaluates. The governance mechanism and the empirical results are developed elsewhere in the arc and are deliberately outside the scope of this paper.

Pratik Sachdeva (UC Berkeley) and Noam Kolt (Hebrew U) have posted “Why AIs (Might) Obey the Law” on SSRN. Here is the abstract:

AI models are no longer confined to producing content and increasingly operate as agents that take actions on behalf of users. A growing body of work empirically tests whether AI models when acting as agents comply with or violate applicable law, including corporate law, tort law, labor law, property law, and contracts. In this paper, we explore a related question: examining why AI models might obey the law. To this end, we draw on and extend the methods for measuring legal compliance pioneered in Tom Tyler’s seminal work, Why People Obey the Law (1990, 2006). Across three studies, we adapt Tyler’s survey methodology—which was originally devised to study the factors explaining human subjects’ compliance with law—to nine AI models. We elicit the AI models’ reported legal compliance alongside the four factors that Tyler proposed to explain compliance: deterrence, morality, peer disapproval, and obligation to obey the law. In Study 1, we find that, when situated as human respondents, AI models report largely homogeneous attitudes toward legal compliance that are broadly comparable to the average human respondent in Tyler’s studies, with one exception: obligation to obey the law diverges sharply across different AI models. In Study 2, we find that demographic conditioning—situating AI models with a particular background (e.g., race, gender)—substantially alters their attitudes toward law, often exaggerating associations Tyler observed in humans and sometimes reproducing stereotyped patterns. In Study 3, we investigate why AI models might themselves obey the law when performing tasks that AI models can undertake in practice. We find that AI models uniformly report near-complete compliance with law, but their attitudes toward law vary substantially: some AI models express a strong sense of obligation to comply with law, while others express a more neutral attitude toward law. Taken together, our methods and results lay the foundation for interrogating the legal compliance of contemporary AI models, as well as shaping the development of future models and their relationship to law.

Alexandra Chouldechova (Carnegie Mellon U H. John Heinz III Public Policy and Management) and Daniel J. Hemel (New York U Law) have posted “Race-Conscious Admissions Algorithms and the Law” on SSRN. Here is the abstract:

In 2023, the U.S. Supreme Court held in Students for Fair Admissions v. Harvard that higher education institutions cannot admit students “on the basis of race.” This article addresses what it means for an admissions algorithm to operate on the basis of race. We develop a taxonomy of race consciousness in the algorithmic decision making context that provides lawyers and machine learning researchers with a shared vocabulary for exploring the implications of the Court’s ruling. We distinguish between “first-order” and “second-order” race consciousness at both the training and predictive phases of machine learning, and we argue that each category of race consciousness raises distinct legal and normative issues. We go on to explain why the Court’s decision need not be read as a flat-out ban on all types of race consciousness in admissions, and why certain forms of race consciousness might even advance the goals of justices who voted to strike down affirmative action policies inSFFA.

Alicia Solow-Niederman (George Washington U Law) has posted “Clickwrap Accountability” (95 Fordham L. Rev. (forthcoming 2026)) on SSRN. Here is the abstract:

Picture a public-facing generative AI chatbot on a government website that can answer questions about benefits eligibility.  Systems like these expose a sociotechnical-legal divide: they route around the standard due process framework, because there has been no formal government determination, yet they induce reliance from users, above and beyond past rounds of automated legal guidance.  Moreover, if there is an error, current doctrine on government errors and equitable estoppel makes relief unlikely.

Thomas A. Smith (U San Diego Law) has posted “Persons Real and Feigned A Metaphysics of the Legal Person” on SSRN. Here is the abstract:

This essay asks what a legal person is, and argues that the law’s many nonhuman persons answer to a single account with three terms and no fourth. A legal person is either a rational substance, an individual being whose own nature is rational, which is what a human being is and the person in the fullest sense; or a unity of order, a body of rational persons held together by an order directed to a common good, which is what a corporation is, and a state, a person not in the full sense but by a true analogy, real and an agent yet possessed of no rational soul of its own; or a pure fiction, a name in the law behind which there stands no person at all, which is what an artificial agent is when the law makes a person of it.

The middle term is the essay’s contribution. Drawn from the metaphysics of Aristotle and Aquinas rather than from the natural law theory of recent decades, the unity of order supplies the category that the fiction, aggregate, and organic theories have each lacked, and it explains how a corporation can truly act, and persist beyond the members who compose it, while having no inner life or conscience of its own. The account refuses to remain idle: it is tested by prediction against the constitutional protections a corporation may and may not claim, across self-incrimination, speech, religious exercise, and the piercing of the corporate veil, and is then carried beyond the business corporation to the state, which is neither a mere aggregate nor a deified substance, and to the artificial agent, which proves to be the first legal person the medieval term persona ficta describes without remaind.

Stephane Rousseau (U Montréal Law) and Catherine Régis (U Montreal Law) have posted “Artificial Intelligence and the Board of Directors: AI Governance Frameworks and Disclosure Practices of Canadian-Listed Corporations” on SSRN. Here is the abstract:

This article examines the state of artificial intelligence (AI) governance among Canadian publicly listed companies, with a focus on the role of boards of directors in overseeing AIrelated risks and opportunities. Drawing on the Canadian corporate governance framework-including directors’ fiduciary duties and duty of care under corporate law, and the comply-or-explain approach adopted by the Canadian Securities Administrators (CSA)-the article situates board oversight of AI within both shareholder primacy and stakeholder theory perspectives. An empirical study of TSX-60 listed companies, based on regulatory filings and structured content analysis, reveals a significant gap between the widespread recognition of AI as a material risk factor and the formal integration of AI governance into corporate architecture. Building on this empirical foundation, the article surveys emerging best practices from academic literature, proxy advisory guidelines, shareholder proposals, and international governance codes. It concludes that existing Canadian governance standards, though partially responsive, remain insufficient to address the complexity and urgency of AI-related challenges. The article recommends that the CSA revise its corporate governance guidelines using the comply-or-explain approach to promote standardized disclosure, reduce fragmentation, and strengthen board accountability in an era of accelerating AI adoption.

Rajat Pandey (MindscopeAi LLP) has posted “The Agentic AI Governance Framework: A Universal Model for Risk, Accountability, and Compliance in Autonomous Systems” (https://orcid.org/0009-0001-8440-2191) on SSRN. Here is the abstract:

The rapid and enthusiastic interest in development and deployment of Agentic AI and especially autonomous agents which are capable of independent reasoning, leveraging and  executing tools, has outpaced the definition of governance mechanism that is required to manage their operational, ethical and regulatory risks.There are existing AI governance models like NIST AI RMF, ISO/IEC 42001, do provide foundational controls however, they are very generic and lack the implementation depth for these conitnuously acting, multi-agent systems.

This paper introduces The Agentic AI Governance Framework, which is a six principled lifecycle model designed to ensure traceability, accountability, and regulatory alignment across agentic deployments. The framework formalizes quantifiable controls through two applied mechanisms: the Agentic Log Retention Index (ALRI), which defines evidenceretention periods based on agent risk and jurisdiction, and a Runtime Governance Architecture integrating human-in-loop escalation, tool-access allow-lists, and modelprovenance capture. Implementation mappings are provided for major orchestration environments including LangChain, Power Platform, and UiPath. 

Adoption of the framework enables organizations to reconstruct agentic decisions within 24 hours, achieve ≥ 95 % provenance coverage, and detect behavioral drift within 10 % variance. The proposed model bridges the current gap between compliance principles and deployable governance practice, establishing a reusable baseline for safe and auditable Agentic AI operations.

David S. Olson (Boston College Law) has posted “Beyond Conception: AI, the America Invents Act, and the Temporal Anchor of Invention” (Brooklyn Law Review, Vol. 92 (Forthcoming)) on SSRN. Here is the abstract:

Artificial intelligence systems increasingly generate technical solutions before any human has anticipated, formulated, or understood them. Under current patent doctrine, these inventions may be unpatentable—not because they fail requirements of novelty, utility, or disclosure, but because they lack a psychologically framed moment of human conception. This Article argues that the continued centrality of conception is both historically contingent and doctrinally unnecessary. Conception arose as a priority-allocating device under the first-to-invent regime. The America Invents Act (AIA) eliminated that regime, replacing it with a first-inventor-to-file system in which priority turns on filing date. Yet conception persists as the definitional anchor of invention, retained by doctrinal inertia rather than functional necessity. Every substantive concern that conception might plausibly serve (screening for operability, ensuring disclosure, preventing trivial patenting) is already performed more directly by existing requirements of utility, enablement, written description, novelty, and obviousness. This Article proposes relocating the temporal anchor of invention from mental formulation to demonstrable technological achievement through actual or constructive reduction to practice. This realignment harmonizes patent doctrine with the AIA’s statutory structure, accommodates computational modes of innovation without inventorship metaphysics, and preserves every existing safeguard against speculative or overbroad patenting.