Cristian Trout (Artificial Intelligence Underwriting Company) has posted “When Does Regulation by Insurance Work? The Case of Frontier AI” on SSRN. Here is the abstract:

No one doubts the utility of insurance for its ability to spread risk or streamline claims management; much debated is when and how insurance uptake can improve welfare byreducing harm, despite moral hazard. Proponents and dissenters of “regulation by insurance” have now documented a number of cases of insurers succeeding or failing to have such anet regulatory effect (in contrast with a net hazard effect). Collecting these examples together and drawing on an extensive economics literature, this Article develops a principled framework for evaluating insurance uptake’s effect in a given context. The presence of certain distortions – including judgment-proofness, competitive dynamics, and behavioral biases – createspotential for a net regulatory effect. How much of that potential gets realized then depends on the type of policyholder, type of risk, type of insurer, and the structure of the insurance market. The analysis suggests regulation by insurance can be particularly effective for catastrophic non-product accidents where market mechanisms provide insufficient discipline and psychological biases are strongest. As a demonstration, the framework is applied to the frontier AI industry, revealing significant potential for a net regulatory effect but also the need for policy intervention to realize that potential. One option is a carefully designed mandate that encourages forming a specialized insurer or mutual, focuses on catastrophic rather than routine risks, and bars pure captives.

Bryant Walker Smith (U South Carolina Joseph F. Rice Law) and Matthew Wansley (Yeshiva U Benjamin N. Cardozo Law) have posted “Regulating Robotaxis” (99 Southern California Law Review 603 (2026)) on SSRN. Here is the abstract:

In several sunbelt cities, commercial robotaxi service has arrived. The leading robotaxi company is providing over 400,000 trips per week. The industry claims that robotaxis will save lives and provide convenient and affordable mobility. Critics counter that they will increase congestion, undermine transit, and subject the public to ubiquitous surveillance. We argue that the social impact of robotaxis depends on how they are regulated. We emphasize two points missing from the debate. First, some of the benefits of robotaxis may be political rather than technological—some longstanding public policy goals may become viable in a robotaxi world. Second, letting one private company dominate the transportation system risks monopoly abuse—and regulators can act now to prevent it.

In this Article, we offer a plan to regulate robotaxis. Carefully crafted externality regulation can address pollution, congestion, wear-and-tear on infrastructure, and privacy risks while minimizing distortions in choices between travel modes. Regulators can promote competition by permitting open entry, banning lock-in contracts, and enabling one-stop access to competing networks. And they can protect riders even if competition fails by mandating that fares be transparent and rider-neutral and requiring that robotaxi companies maintain a fleet sufficient for emergencies. Policymakers should take advantage of robotaxi deployment to reimagine the transportation system—liberate land from the tyranny of parking, refocus mass transit investments on high-throughput routes, and expand mobility for people with low incomes and people with disabilities.

Nydia Remolina (Singapore Management U Yong Pung How Law) has posted “Agentic Payments: When is a Payment (Un)Authorised?” on SSRN. Here is the abstract:

What if your wallet could decide how to spend your money? Payments are no longer made by people alone, they are increasingly executed by software acting on their behalf. These AI agents operate within user-defined parameters, but they also optimise, adapt, and, at times, act in ways the user did not specifically anticipate. This shift challenges the core assumptions of payment law, which remains anchored in binary distinctions between authorised and unauthorised transactions and in models of human intent. Current regulations like PSD2, and the proposed PSR and PSD3, struggle to define when a broad user instruction constitutes valid permission for a specific payment. To bridge this gap, the paper proposes a “bounded delegated authorisation” test, which requires that an agent’s actions stay within strictly defined, provable constraints. The paper also suggests creating a new regulatory category for Digital Assistant Payment Services (DAPS) to ensure accountability and user control. Ultimately, the article argues for a framework where payment service providers must reimburse users if an AI agent initiates a transaction that exceeds its bounded delegated authorisation.

Jessica Tillipman (George Washington U Law) has posted “What Rights Do AI Companies Have in Government Contracts?” (Nextgov/FCW (2026).) on SSRN. Here is the abstract:

The Anthropic-Pentagon dispute has generated widespread commentary but fundamental confusion about whether contractors can restrict the government’s use of their products. This article argues the question is not novel. The scope of permissible restrictions depends on the acquisition pathway, the contract type, and the negotiated terms. The article surveys the principal pathways through which the federal government acquires AI and examines OpenAI’s published Pentagon contract language, which adopts an “any lawful use” standard conditioned on existing legal authorities. A critical tension emerges: although the contract facially permits broad use, OpenAI’s retained architectural control over its cloud-only deployment and safety infrastructure may impose practical constraints exceeding those Anthropic sought through express contractual restrictions. The article concludes that the public debate has focused on the wrong question. The more consequential governance failure is the government’s inability to secure adequate transparency, audit rights, and safeguards when procuring AI through commercial pathways not designed for technologies this complex and consequential.

Huijuan Peng (Singapore Management U Yong Pung How Law) and Pey-woan Lee (Singapore Management U Yong Pung How Law) have posted “Reimagining U.S. Tort Law for Deepfake Harms: Comparative Insights from China and Singapore” (Journal of Tort Law, 0[10.1515/jtl-2025-0028]) on SSRN. Here is the abstract:

This Article explores how U.S. tort law can respond more effectively to the distinct harms posed by deepfakes, including reputational injury, identity appropriation, and emotional distress. Traditional tort doctrines, such as defamation, the right of publicity, and intentional infliction of emotional distress (IIED), remain fragmented and ill-suited to the speed, scale, and anonymity of deepfake dissemination. Using a comparative functionalist approach, the Article analyzes how China and Singapore respond to deepfake harms through structurally divergent but functionally instructive frameworks. China’s model combines codified personality rights with intermediary obligations under a civil law regime, while Singapore adopts a hybrid approach that integrates common law torts with targeted statutory and administrative interventions. Although neither model is directly replicable in the United States, both offer valuable comparative insights to guide the reform of U.S. tort law. The article advances an integrated governance model for U.S. tort law: reconstructing personality-based torts, repositioning tort law through conditional intermediary liability, and clarifying constitutionally grounded limits for speechbased claims. Drawing on Chinese and Singaporean legal approaches, the Article sets out a comparative reform framework that enables U.S. tort law to better address deepfake harms while safeguarding autonomy and dignity in AI-driven digital environments.

Zahra Takhshid (U Denver Sturm College Law) has posted “Virtual Dignitary Torts” (The Journal of Tort Law forthcoming in Volume 18 Issue 1, 2025) on SSRN. Here is the abstract:

The emergence of the metaverse and spatial computing, which has enabled immersive digital interactions, raise complex legal questions. This work examines the feasibility of addressing dignitary torts-such as battery and intentional infliction of emotional distress-committed via avatars. The particular challenge for tort law is the nonphysical nature of selfrepresentations in these virtual spaces. Drawing from the historical evolutions of several dignitary torts, such as the law of battery and emotional harm, this article argues that the key in allowing for the recognition of such harms is appreciating the expansion of the protection of physical body within these torts, to the protection of a broader concept of the “self.” By this, tort law has demonstrated both its willingness and capacity to recognize new forms of wrongs without sacrificing its core principles. Accordingly, this essay lays the groundwork for recognizing harms in virtual spaces and offers several initial considerations for dignitary tort liability regime and the extension of the self in extended reality spaces. Bridging the gap between evolving technology and traditional tort law is a must in a world where virtual interactions are carrying increasingly real consequences.

Choonsik Lee (U Rhode Island) and Matthew E. Souther (U South Carolina Darla Moore Business) have posted “Beyond Bias: AI as a Proxy Advisor” on SSRN. Here is the abstract:

After documenting a trend towards increasingly subjective proxy advisor voting guidelines, we evaluate the use of artificial intelligence as an unbiased proxy advisor for shareholder proposals. Using ISS guidelines, our AI model produces voting recommendations that match ISS in 79% of proposals and better predicts shareholder support than ISS recommendations alone. Disagreements between AI and ISS are more likely when firms disclose hiring a third-party governance consultant, suggesting these consultants-often the proxy advisor itself-may influence recommendations. These findings offer insight into proxy advisor conflicts of interest and demonstrate AI’s potential to improve transparency and objectivity in voting decisions.

Christina Lee (George Washington U Law) has posted “Beyond Algorithmic Disgorgement: Remedying Algorithmic Harms” (16 U.C. Irvine Law Review ___ (forthcoming 2026)) on SSRN. Here is the abstract:

AI regulations are popping up around the world, and they mostly involve ex-ante risk assessment and mitigating those risks. But even with careful risk assessment, harms inevitably occur. This leads to algorithmic remedies: what to do once algorithmic harms occur, especially when traditional remedies are ineffective. What makes a particular algorithmic remedy appropriate for a given algorithmic harm?

I explore this question through case study of a prominent algorithmic remedy: algorithmic disgorgement—destruction of models tainted by illegality. Since the FTC first used it in 2019, it has garnered significant attention, and other enforcers and litigants around the country and the world have started to invoke it. Alongside its increasing popularity came a significant expansion in scope. Initially, the FTC invoked it in cases where data was allegedly collected unlawfully and ordered deletion of models created using such data. The remedy’s scope has since expanded; regulators and litigants now invoke it against AI whose use, not creation, causes harm. It has become a remedy many turn to for all things algorithmic.

I examine this remedy with a critical eye, concluding that though it looms large, it is often inappropriate. Algorithmic disgorgement has evolved into two distinct remedies. Data-based algorithmic disgorgement seeks to remedy harms committed during a model’s creation; use-based algorithmic disgorgement seeks to remedy harms caused by a model’s use. These two remedies aim to vindicate different principles underlying traditional remedies: data-based algorithmic disgorgement follows the disgorgement principle underlying remedies like monetary disgorgement and the exclusionary rule, while use-based algorithmic disgorgement follows the consumer protection principle underlying remedies like product recall. However, they often fail to live up to the principles. AI systems exist in the context of the algorithmic supply chain; they are controlled by many hands, and seemingly unrelated entities are connected to each other in complicated ways through complex data flows. The realities of algorithmic supply chain means that algorithmic disgorgement is often a bad fit for the harm at issue and causes undesirable effects throughout the algorithmic supply chain, imposing burden on innocent parties while not imposing cost on the blameworthy; ultimately, algorithmic disgorgement undermines the principles it seeks to promote.

From this analysis, I derive considerations for determining whether an algorithmic remedy is appropriate—the responsiveness of the remedy to the harm and the full impact of the remedy throughout the supply chain—and underscore the need for a diversity of algorithmic remedies.

Bryan H. Choi (Ohio State U (OSU) Michael E. Moritz College Law) has posted “Tainted Source Code” (39 Harv. J.L. & Tech. (2025)) on SSRN. Here is the abstract:

Open-source software has long eluded tort liability. Fierce ideological commitments and sticky license terms support a long tradition of forbearance against penalizing harmful or negligent work in open-source communities. The free, noncommercial, distributed, and anonymous characteristics of open-source contributions present additional obstacles to legal enforcement.

The exponential rise in software supply chain attacks has given new urgency to the problem of bad open-source code. Yet, current approaches are unlikely to meaningfully improve open-source security and safety. On the one hand, technological tools and self-governance mechanisms remain woefully underdeveloped and underutilized. On the other hand, liability proposals that place all the burden on commercial vendors to inspect the open-source packages they use is an impractical solution that ignores how software is built and maintained.

This Article argues that donated code should be subject to tort liability by analogy to the law of tainted food and blood donations. Food safety law is the progenitor of modern tort law, and it reveals an older set of tensions between altruistic efforts to address societal hunger and the need for accountability in regulating the quality of food supply chains. At common law, the charitable nature of a donation is a nonfactor in determining liability. Legislatures have intervened to provide safe harbors, but only up to an extent. This nuanced history offers a principled path forward for extending a liability framework to donations of open-source code.

Ayelet Gordon-Tapiero (Hebrew U Jerusalem Benin Computer Science and Engineering) has posted “A Liability Framework for AI Companions” (1 Geo. Wash. J. L. & Tech (Forthcoming)) on SSRN. Here is the abstract:

Everyday tens of millions of people engage in online conversations. These virtual interactions range from casual chats about daily life to deeply personal exchanges, where individuals share secrets, vulnerabilities, sexual fantasies, hopes and dreams. Through these conversations users receive emotional support and empathetic responses, get practical advice and productivity tips. Most importantly they feel seen, heard, and less alone. These people are not chatting with friends or family members. They are corresponding with AI-powered chatbots, also known as AI companions, which have gained immense popularity recently. AI companions offer a range of benefits to users including providing a feeling of friendship, emotional support, and organization of everyday tasks. But AI companions also harbor a darker side. They are designed by large corporations with the goal of maximizing their profits and collecting more data on which to train future models. Users often find themselves subject to manipulation, growing emotional dependence, and even addiction. Tragically, it is the most vulnerable users that are most susceptible to these harms. In a horrific case, a teenager even took his own life after being encouraged to do so by his AI companion.

Against this backdrop, this Article argues for the urgent need to develop a comprehensive legal response to the emerging ecosystem of AI companions. Specifically, it proposes applying products-liability law to AI companions as a promising legal avenue. This Article also offers a typology of the promises and perils associated with the use of AI companions. Recognizing both the benefits and harms stemming from a technology is a crucial first step in crafting a regulatory response that preserves its advantages while mitigating its risks.

AI companions are designed to maximize the profits of the companies that develop them by facilitating engagement and fostering dependency, which can lead to addiction. In this reality, users’ interests are secondary at best. Courts have long recognized two types of product defects that can give rise to liability: design defects and failure to warn. Thus, an AI companion designed to maximize user engagement, encourage user-dependence and facilitate addiction could be considered to have been defectively designed. Similarly, companies deploying AI companions known to harm vulnerable users should, at the very least, warn them of these risks.

Products-liability law offers an appropriate and necessary framework for addressing the challenges posed by AI companions. It allows courts to gradually establish standards for what should be considered a defective product, while holding companies accountable for their failure to warn users about potential dangers. This approach incentivizes companies to design safer products, limiting the harms generated by AI companions, while allowing users to continue enjoying the benefits offered by them.