King on Feeding the Beast: Control of Healthcare Data as a New Indicator of Market Power?

Jaime S. King (Law) has posted “Feeding the Beast: Control of Healthcare Data as a New Indicator of Market Power?” ((2026) St Louis University Journal of Health Law and Policy) on SSRN. Here is the abstract:

As in nearly all aspects of modern life, artificial intelligence (AI) is poised to bring dramatic changes to healthcare. The literature has well documented the promise and perils of AI use in healthcare, yet few discussions consider the impact it will have on healthcare markets and competition. While the last two decades have seen the rise of healthcare systems and large national insurers as the dominant players in healthcare, we are now in the midst of a shift in power in healthcare markets from one focused on size and scope of health entities to one focused on information. Driven by the growing use of AI in healthcare, this shift has significant implications for antitrust enforcement and merger review which will require additional oversight to protect competition and consumers. The article proceeds in four parts. Part I will briefly review changes in healthcare markets and market power over the last twenty years. Part II analyzes the intersection of big data and AI with healthcare and illuminates the rise of information power. Part III considers the implications of this intersection for healthcare markets and consumers. Finally, Part IV provides some initial recommendations for expanded antitrust oversight including new notice and review requirements for partnerships, mergers, and acquisitions between healthcare and AI entities, expanded use of antitrust tools to address new cross-industry and market dynamics, and broader collaboration between governmental agencies to address quickly evolving AI capabilities. This article is a call for greater attention to and oversight of the rapid development of partnerships between industry giants in healthcare and technology to meet the demands for HealthAI and the implications these transactions have for competition, consumers, and the corporations that will hold our most intimate information.

Gibson et al. on Slowing Down AI with IP

James Gibson (U Richmond Law) and Christopher Anthony Cotropia (George Washington U -Law) have posted “Slowing Down AI with IP” on SSRN. Here is the abstract:

The current artificial intelligence (AI) landscape is defined by “too much, too fast”: rapid, winner-take-all scaling by a handful of firms, backed by states and capital markets, with little transparency into training data, model architectures, or deployment practices. The resulting harms are widely recognized: environmental damage, labor displacement, economic instability, privacy violations, and perpetuation of bias.

What is not widely recognized is the role that intellectual property (IP) can play in mitigating these harms. Existing policy debates largely treat IP protection as an AI accelerant, framing patent and copyright as incentives for more and faster AI innovation. If this were true, granting IP rights to AI innovators would only make things worse. But this Article inverts the conventional framing. When it comes to AI development, IP’s incentive effect is minimal, whereas IP’s well-known costs—e.g., reduced production, slower diffusion, and mandatory disclosure—predominate.

Maintaining and expanding IP protection for AI technologies therefore has the opposite of its usual effect: it slows down innovation. This makes it a uniquely fitting regulatory tool for the “too much, too fast” AI landscape. And while IP is by no means a cure-all, it has unexpected advantages over more common regulatory strategies. Overall, we reposition IP not as fuel for the AI race, but as a necessary brake on a runaway industry.

Dalton on A Problem-Solving Approach to International AI Governance

Taylor R. Dalton (Santa Clara U Law) has posted “A Problem-Solving Approach to International AI Governance” on SSRN. Here is the abstract:

Should international law be used to govern the development and use of artificial intelligence (AI) technologies? This is a crucial question as developments in AI have been speeding forward in many countries around the world. Yet, in parallel to the excitement around the developments of AI, concerns have arisen about the impact these tools will have on everything from high school homework to nuclear war. Beyond national regulation, many have called for the international community to come together to craft governance rules and norms that will mitigate some of the harmful effects of AI in the future.

Although international governance of this technology may be desirable, it is less apparent whether international institutions (like treaties) could or should be established to regulate something as potentially ubiquitous as AI. This essay analogizes the development of new and anticipated AI technologies to past technological developments to present various categories of cooperation or coordination problems related to those technologies. Invoking theories of rational institutional design, the essay first focuses on what type of problems the international system could or should solve in relation to AI. It then advocates for channeling efforts into more targeted international regimes that address discrete problems, using the problems that may arise in the area of armed conflict as a suitable example. AI’s impact on armed conflict is ripe for public regulation at the international level, especially in the context of autonomous weapons systems. The approach presented here, elevates tested strategies for solving cooperation problems that have succeeded in the past. Additionally, focusing on specific cooperation problems created by AI risks may narrow the bargaining space between countries and allow for agreement on the most feasible and meaningful tasks in the near term. These insights bridge our understanding of international relations and law to provide a framework for a feasible path forward.

Colangelo on Is AI the End of the DMA as we Know It?

Giuseppe Colangelo (Università degli Studi della Basilicata) has posted “Is AI the End of the DMA as we Know It?” on SSRN. Here is the abstract:

The disruptive potential of AI-enabled applications for competitive dynamics and the core organisational forms of digital intermediation inevitably also has significant implications for the recent regulatory initiatives adopted to govern digital markets. Indeed, because these instruments were conceived without AI specifically in view, they risk becoming outdated within a very short period of time. Notably, while they have been shaped by a Big Tech-centred conception of digital markets, the possible emergence of new gatekeepers in the age of AI marks a turning point that calls into question the very rationale and foundations of these regimes in their present form. As a result, only a few years after its enactment, the role of the DMA, together with its rationale and claimed future-proof character, is already under scrutiny, as the deployment of AI applications raises the question whether policymakers should reopen the legislative framework in order to amend the Regulation. Against this background and in the context of the first review of the DMA, the paper argues that the rise of AI applications calls for a reconsideration of the DMA’s overall architecture and for the development of a distinct competition policy framework, rather than for a merely incremental fine-tuning exercise.

Chesterman on From Slaves to Synths? Superintelligence and the Evolution of Legal Personality

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.

Arbel et al. on AI Nationalization

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.

Mahamud et al. on Connected and Autonomous Vehicle Testing in Rural America: A Review of U.S. State Legislation, Challenges, and Opportunities

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. The foundational step toward the successful testing and deployment of CAVs begins with establishing a clear and comprehensive regulatory framework. A well-defined legislative environment not only provides clarity for technology developers and public agencies but also ensures safety, consistency, and public trust. This paper analyzes the challenges and opportunities of operating CAVs in the Upper Great Plains rural states. This study provides a comprehensive analysis that evaluates the legislative, regulatory, and technical prerequisites essential for deployment across the region’s highway corridors. An in-depth review of current state and federal regulations, policies, and safety standards identifies key legal and regulatory gaps, as well as infrastructure limitations. Findings highlight the critical need for harmonized regulations, targeted infrastructure investments, and advancements in traffic management and communication technologies. The comparative analysis revealed gaps in testing permits, liability frameworks, and broadband-enabled traffic control, leading to targeted recommendations. The recommendations include harmonized permitting processes, standardized liability provisions, and investment in connected signal systems to enhance mobility, safety, and traffic flow in underserved rural regions. Moreover, recommendations highlighted several needed legislative updates, infrastructure improvements, and cross-sector coordination to support policymakers in promoting the safe, efficient, and sustainable implementation of CAV technology within the Upper Great Plains region.

Kolt et al. on Lessons from Complex Systems Science for AI Governance

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.

Khan et al. on Designing an Ethical Multimodal Driver Monitoring Systems: Risk Mitigation, Incident Response, and Accountability in Automated Vehicles

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. While technologically promising, their deployment introduces a complex set of ethical and legal challenges – ranging from privacy and consent to data ownership and algorithmic fairness. While overarching frameworks such as the GDPR, EU AI Act, and IEEE standards offer important guidance, they lack the specificity required for addressing the unique risks posed by in-cabin sensing technologies. This paper addresses this gap by identifying key ethical challenges specific to AI-powered DMS and proposing a comprehensive, modular framework for ethical design, deployment, and governance. It introduces practical design responses – such as user-configurable consent pathways, fairness-aware model training, explainability tools, and emotional well-being safeguards – grounded in established legal and ethical principles. Finally, the paper outlines a risk analysis and failure mitigation strategy, emphasizing proactive incident response and accountability mechanisms tailored to the DMS context. Together, these contributions aim to inform the development of transparent, trustworthy, and human-centered driver monitoring systems for next-generation autonomous vehicles.

Chen on Contingent AI Welfare Policies

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.