Richards on Privacy Is Not Theft

Neil M. Richards (Washington U Law) has posted “Privacy Is Not Theft” on SSRN. Here is the abstract:

Something curious has happened to privacy recently. In our post-pandemic age of generative AI, tech companies and governments have stopped asking for our data or relying on fictions of implied consent. They have simply started taking it, arguing that data collection is inevitable, necessary, and virtuous. As masked federal agents prowl American streets sticking facial recognition scanners in the faces of suspected undocumented aliens and as AI companies lament the lack of personal data to train their foundation models, there is a sense that some think privacy is getting in the way of the future. In their view, protections for human data are a kind of theft against convenience, a theft against safety, and a theft from the stockpile of personal data our machines need to create a frictionless future of leisure, abundance, and entertainment.

We’ve seen this idea before, but until recently only in the realms of farce and dystopian satire. Dave Eggers’ 2013 novel The Circle envisioned a sinister social network—a kind of Facebook on steroids—whose founders espoused a series of silly and dystopian mantras, including the idea that “Privacy Is Theft.” In the novel, the company uses these principles as the basis for their business, and by extension, a radically anti-privacy reworking of near-future society. Foreseeably dystopian hijinks ensue. Life imitates art, indeed, as a series of products and practices in our non-fictional world have begun to reflect the mantra that “Privacy Is Theft,” suggesting that indiscriminate data collection is both (1) inevitable and (2) virtuous.

This essay uses Jonathon Penney’s new book on chilling effects in connection with the broader trans-disciplinary and trans-Atlantic scholarship on what privacy is and why it matters to critique the way some powerful entities have started to talk about privacy as a kind of theft.  Penney’s work shows the hollowness of these curious suggestions that privacy is no longer a fundamental right but rather a kind of theft from the future, or at least a theft from people who think they are building a most excellent future for themselves and want your information as raw material to do that. The essay presents a few recent technological developments, explains the ideology of Privacy-as-theft that underlies them, and suggests that Penney’s work allows us to better deconstruct that ideology and helps us to understand why they it is problematic, and how the law is failing us—at least if we still care about things like democracy, eccentric individuality, and the development of new, heretical ideas. My claim here is a simple one—that privacy is not actually theft, but an essential value that we need to protect if we want to continue to live in societies that preserve individual rights, self-government diversity, and just plain eccentric weirdness. The real thieves are the ones who want your data and complicity and who are building unnecessary tools to steal it.

My argument proceeds in four steps. Part I presents the ideology of Privacy As Theft in The Circle. Part II discusses three new technological developments embodying this shift—Meta’s aggressive marketing of its “smart” glasses, the use of facial recognition tools by ICE personnel seeking undocumented immigrants, and Zoom’s presumption that all meetings should be transcribed and assessed by its artificial intelligence tools. Part III explores Penney’s work on chilling effects in the context of the broader literature, showing how at the level of theory and at that of empirical social science, privacy is necessary to any society that aspires to individuality, eccentricity, and/or political freedom. Part IV shows how the ideology of Privacy As Theft is not just hogwash, but hogwash in pursuit of a much greater theft of its own—the power to dictate what kind of society we will inhabit in the future.

Marchant et al. on Private Standards as Liability Shields: A Pro-Innovation Artificial Intelligence Regulatory Approach for States

Gary E. Marchant (Arizona State U College Law) and Jordan Buckwald (Independent) have posted “Private Standards as Liability Shields: A Pro-Innovation Artificial Intelligence Regulatory Approach for States” on SSRN. Here is the abstract:

States face a dilemma. The federal government is not regulating artificial intelligence (AI), and is threatening states that regulate the technology with preemption and funding restrictions. Moreover, piecemeal state regulation with different substantive requirements risks impeding AI innovation and harming our national interest and security. And yet, AI creates a whole host of problems relating to accuracy, safety, security, bias, transparency, privacy, and autonomy that needs to be governed. This Article presents a solution to this dilemma that can protect against AI risks without harming innovation and the national interest. The proposal is for states to provide a liability shield for AI systems that conform to recognized comprehensive risk management standards. After discussing the proposed solution, as well as drawbacks to the model, the analysis concludes that these liability shields are a simple, positive step that states can take to both promote AI innovation and protect their citizens from risks.

Magnuson on Artificially Intelligent Markets

William J. Magnuson (Texas A&M U Law) has posted “Artificially Intelligent Markets” (Harvard Business Law Review, Vol. 16, Forthcoming) on SSRN. Here is the abstract:

A remarkable transformation is taking place in our financial markets.  The rise of machine learning algorithms and other artificial intelligence models has rapidly overtaken older methods of financial decisionmaking, and the consequences of the revolution are beginning to be felt across the capital markets ecosystem, from stock exchanges to derivatives markets to currency trading.  These new technologies offer great promise, including more accurate prices, faster transactions and more efficient trading.  But they also create risks.  From flash crashes to insider trading algorithms to adversarial attacks, artificial intelligence presents a range of unique vulnerabilities that could lead to significant and wide-ranging harm to our financial system.  Legal frameworks devised to structure and constrain financial institutions, in turn, are ill-equipped to deal with these harms because they were designed based on outdated assumptions about the structure of markets, as well as the nature of its primary actors. This Article offers the first comprehensive account of the economic, political and legal consequences of the rise of artificially intelligent markets.  It demonstrates how the major driver of this shift has been the hedge fund industry, an opaque and lightly regulated sector of the financial ecosystem that has long been an early-adopter of financial technology.  It concludes by proposing a series of escalating regulatory reforms that might better fit financial regulation to our new artificially intelligent markets.

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.