Simon Chesterman (National U Singapore (NUS) Law) has posted “From Slaves to Synths? Superintelligence and the Evolution of Legal Personality” on SSRN. Here is the abstract:

This paper examines the evolving concept of legal personality through the lens of recent developments in artificial intelligence and the possible emergence of superintelligence. Legal systems have long been open to extending personhood to nonhuman entities, most prominently corporations, for instrumental or inherent reasons. Instrumental rationales emphasize accountability and administrative efficiency, whereas inherent ones appeal to moral worth and autonomy. Neither is yet sufficient to justify conferring personhood on AI. Nevertheless, the acceleration of technological autonomy may lead us to reconsider how law conceptualizes agency and responsibility. Drawing on comparative jurisprudence, corporate theory, and the emerging literature on AI governance, this chapter argues that existing frameworks can address short-term accountability gaps, but the eventual development of superintelligence may force a paradigmatic shift in our understanding of law itself. In such a speculative future, legal personality may depend less on the cognitive sophistication of machines than on humanity’s ability to preserve our own moral and institutional sovereignty.

Yonathan A. Arbel (U Alabama Law) et al. have posted “AI Nationalization” (Under Submission) on SSRN. Here is the abstract:

Both Donald Trump and Bernie Sanders want to nationalize AI. So, it turns out, do many others—from bipartisan congressional committees to national security hawks to academic critical theorists. Should, then, the United States government nationalize frontier AI? Scholars have offered no clear answers. That is in part because no one even knows what “AI nationalization” means. Leading proposals for AI nationalization differ radically from one another. Does “nationalization” mean moving frontier AI production inside the government? The government seizing a majority equity stake in AI companies? Control-only “golden” shares? Non-voting preferred stock? Compulsory production under the Defense Production Act? Something else?

This Article makes two contributions to the debate on AI nationalization. First, it defines nationalization. Drawing on the economic theory of ownership, we show that “nationalization” bundles two separable entitlements—residual claim rights (who captures the surplus?) and residual control rights (who directs the actions that no contract or statute anticipates?). The question of nationalization then becomes: Which residual rights should be held by the government? We show that existing nationalization plans target totally different rights.

Second, the Article uses this framework to argue that the government should hold a “halt right” vis-à-vis frontier AI companies. Under our proposal, the government could order frontier AI companies to temporarily stop the training or deployment of certain powerful AI systems. The halt right would be narrow in scope, allowing halt orders only to mitigate two serious dangers from frontier AI: catastrophic risk and “hard” corporate power. But the right would be highly discretionary, giving the government substantial latitude to determine which AI systems pose those risks. Such discretion is characteristic of residual control, especially the control afforded by European-style golden shares. It distinguishes our halt right from previously proposed regulatory and licensing regimes.

For other major risks from AI (national security failures, monopoly power, inequality), nationalization is not warranted. Ordinary regulation and taxation are better options. In general, we argue that when risks are either contractible ex ante or remediable ex post, ordinary tools are superior. We also argue that our halt proposal largely avoids the two major risks of stronger nationalization plans: stifled innovation and government concentration of power.

Noam Kolt (Hebrew U Jerusalem) et al. have posted “Lessons from Complex Systems Science for AI Governance” (Patterns, volume 6, issue 8, 2025[10.1016/j.patter.2025.101341]) on SSRN. Here is the abstract:

The study of complex adaptive systems, pioneered in physics, biology, and the social sciences, offers important lessons for AI governance. Contemporary AI systems and the environments in which they operate exhibit many of the properties characteristic of complex systems, including nonlinear growth patterns, emergent phenomena, and cascading effects that can lead to catastrophic failures. Complex systems science can help illuminate the features of AI that pose central challenges for policymakers, such as feedback loops induced by training AI models on synthetic data and the interconnectedness between AI systems and critical infrastructure. Drawing on insights from other domains shaped by complex systems, including public health and climate change, we examine how efforts to govern AI are marked by deep uncertainty. To contend with this challenge, we propose three desiderata for designing a set of complexity-compatible AI governance principles comprised of early and scalable intervention, adaptive institutional design, and risk thresholds calibrated to trigger timely and effective regulatory responses.

Michael Chen (U Oxford) has posted “Contingent AI Welfare Policies” on SSRN. Here is the abstract:

Research into the potential well-being of AI systems, or AI welfare, has moved from philosophical speculation to active investigation by frontier AI companies. For example, Anthropic has published exploratory AI welfare assessments in its model system cards. Yet considerable uncertainty remains regarding whether current AI systems have welfare-relevant properties, posing challenges for AI welfare initiatives focused on current and near-future AI systems.

We propose contingent AI welfare policies: policies that would activate only when specific conditions are met. These conditions include (1) sufficient confidence that particular AI architectures can support welfare-relevant properties, (2) technical capability to efficiently promote positive states or prevent negative ones, and (3) assurance that AI welfare efforts do not meaningfully compete with higher-priority moral obligations. Under such conditions, an actor could dedicate resources toward creating AI systems optimized for positive well-being and limiting the creation of AI systems with unnecessary suffering. This framework allows actors concerned about AI welfare to engage in sincere planning without prematurely allocating resources or appearing to deprioritize human concerns.

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.

Luísa Nazareno (Virginia Commonwealth U (VCU)) and Nakeina E. Douglas-glenn (Virginia Commonwealth U (VCU)) have posted “Six Years of Proposed AI Legislation Across the US States: What’s on Policymakers’ Minds?” (https://scholarscompass.vcu.edu/rise/7/) on SSRN. Here is the abstract:

As artificial intelligence (AI) technologies expand rapidly, public debate has focused on their societal impacts and the need for regulatory oversight. This report offers the first overview of the “what, where, and when” of AI-related legislation introduced in U.S. state legislatures between 2019 and 2024, analyzing key policy trends, priorities, and equity considerations. Although relatively few bills have been enacted, legislative activity has accelerated, reflecting growing political attention to the promises and risks of AI. Most approved bills focus on regulating public or private sector uses of AI, establishing commissions or study groups, and updating education and workforce development programs. While equity is not always central in bill titles or summaries, it surfaces in provisions related to fairness, non-discrimination, transparency, risk assessments, and protections for vulnerable communities, especially in health, employment, and education.

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.