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.

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.

Md Ashik Mahamud (U North Dakota) and Sherif M. Gaweesh (U North Dakota) have posted “Connected and Autonomous Vehicle Testing in Rural America: A Review of U.S. State Legislation, Challenges, and Opportunities” on SSRN. Here is the abstract: The rapid advancement of connected and autonomous vehicle (CAV) technology has the potential to revolutionize transportation systems. However, it also presents unique challenges, especially in rural states, characterized by low population and relatively lower traffic volumes.

Noam Kolt (Hebrew U Jerusalem) et al. have posted “Lessons from Complex Systems Science for AI Governance” (Patterns, volume 6, issue 8, 2025) 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.

Bilal Alam Khan (Trinity College (Dublin)) et al. have posted “Designing an Ethical Multimodal Driver Monitoring Systems: Risk Mitigation, Incident Response, and Accountability in Automated Vehicles” on SSRN. Here is the abstract: As vehicles transition toward higher levels of automation, Driver Monitoring Systems (DMS) have become essential for ensuring human oversight, safety, and regulatory compliance. These systems rely on multimodal sensing and AI-driven inference to assess driver attention, cognitive state, and readiness to take control.

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.

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?

Saahir Vazirani (The Perrin Research Institution) has posted “Autonomy in Conflict: Legal, Technical, and Strategic Challenges of AI-Enabled Autonomous Weapon Systems Under International Humanitarian Law” on SSRN. Here is the abstract: War has historically acted as a catalyst for new technologies, driving advancements that have transformed society. From the creation of nuclear weapons to the establishment of cyber warfare teams, the pressures of war have expedited technological development while also forcing often controversial ethical discussions.

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?” ( 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.

Lars Hornuf (Dresden U Technology) et al. have posted “Regulatory Competition in the Age of AI” on SSRN. Here is the abstract: Artificial Intelligence (AI) has not only triggered a technology race and a fight for market leadership, but has also become a site of regulatory competition. As governments race to define rules for AI, they embed competing visions of alignment, innovation, and control into digital technology.

Michael Chatzipanagiotis (U Cyprus) has posted “Incident Reporting and Investigation under the AI Act: Some Insights from Aviation” (International Journal Of Law And Information Technology, forthcoming) on SSRN. Here is the abstract: This article examines the provisions of the EU AI Act (AIA) on incident reporting and investigation and explores how the regulatory context of the AIA could benefit from the experience of aviation.

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?

Rick Roth (Government the United States America Naval Postgraduate) has posted “Concept Programming for Dependable AI” on SSRN. Here is the abstract: Large language models deployed in critical systems today have no principled method for instilling specified behavioral concepts, whether explicit safety constraints, cultural values, professional norms, or governance principles, that generalize dependably across novel contexts. Such systems are termed Language-Grounded Neural Systems (LGNS) throughout this paper.

Ketan Ramakrishnan (Yale U Law) has posted “Tort Law at the Frontier of Artificial Intelligence” (Yale Journal on Regulation (forthcoming 2026)) on SSRN. Here is the abstract: The frontier of contemporary AI development is dominated by AI systems built on foundation models - highly versatile algorithms, trained in the first instance on broad swathes of data, that can function as tools and agents across a wide variety of commercial, social, military and political domains.

Rajat Pandey (MindscopeAi LLP) has posted “The Agentic AI Governance Framework: A Universal Model for Risk, Accountability, and Compliance in Autonomous Systems” ( 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.

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.

Paul Jurcys (U California) has posted “Copyright Registration Requirement in the U.S.” on SSRN. Here is the abstract: This entry, prepared for the Elgar Encyclopedia of Intellectual Property Law (2026), provides an overview of copyright registration requirements in the United States. It explains that, unlike patents or trademarks, copyright protection in the U.S. arises automatically upon the creation of an original work fixed in a tangible medium.

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.

Andrew Blair-Stanek (U Maryland Francis King Carey Law) et al. have posted “Is AI's Law School Exam Performance Plateauing?” on SSRN. Here is the abstract: Last spring, we had OpenAI's reasoning model o3 take our final exams, with the reasoning effort parameter set to "high," and graded its answers on the same curve as our students. o3 got grades ranging from A+ to B.

John Ferguson (The Ferguson Clinic) has posted “Personal Medical AI: A Framework for Individual-Based Healthcare Monitoring SubTitile: Personal Medical AI Framework” on SSRN. Here is the abstract: Current healthcare AI systems compare patient data against population norms, potentially missing clinically significant deviations that are abnormal for specific individuals. We propose a framework for personal medical AI that establishes individual baselines, learns patient-specific patterns, and detects deviations meaningful to each patient rather than comparing against population averages.

Bj Ard (U Wisconsin Law) and Rebecca Crootof (U Richmond Law) have posted “Technology Law Chapter 6: Upset Equilibria” on SSRN. Here is the abstract: Based on years of experience teaching the subject, we have produced a first draft of a “Technology Law” coursebook. It teases out fundamental concepts, introduces our methodology for resolving tech-fostered legal uncertainties, and identifies the strengths and weaknesses of different regulatory choices.

Michael Veale (U College London Laws) and João Pedro Quintais (U Amsterdam Institute Information Law (IViR)) have posted “The Obligations of Providers of General-Purpose AI Models” on SSRN. Here is the abstract: During the legislative process, the EU Artificial Intelligence (AI) Act was amended to include provisions related to general-purpose AI (GPAI) models. These broadly relate to transparency towards downstream users and relevant regulators, in addition to obligations connected to intellectual property.

Mohammad Shahidullah (International American U (IAU)) et al. have posted “Intelligent Fraud Detection: Applying Advanced Analytics and Cybersecurity Insights in U.S. Finance” (Journal of Posthumanism, volume 4, issue 3, 2024 ) on SSRN. Here is the abstract: Fraud detection in financial transactions is a major and crucial problem that does not cease to exist, mainly because of the enormous imbalance in the datasets obtained and the very high requirement for an accurate distinction between legitimate and fraudulent activities.

Daniel Schwarcz (U Minnesota Law) has posted “Distributing Risk in an Age of AI: Procedural Bad Faith and AI Claims Handling” on SSRN. Here is the abstract: Written in honor of Kenneth Abraham and his foundational contributions to insurance law, this Essay argues that the rise of AI-driven insurance claims handling exposes a significant gap in first-party bad faith law. Traditional bad faith doctrine has focused primarily on outcomes, asking whether an insurer wrongfully denied or delayed payment of benefits owed under the policy.

Marco Pollanen (Trent U) has posted “The Probabilistic Foundations of Surveillance Failure: From False Alerts to Structural Bias” on SSRN. Here is the abstract: For decades, forensic statisticians have debated whether searching large DNA databases undermines the evidential value of a match. Modern surveillance faces an exponentially harder problem: screening populations across thousands of attributes using threshold rules rather than exact matching.