Technology

Recently, many states have reacted to the growing data economy by passing data privacy statutes. These follow the “interaction model”: they allow consumers to exercise privacy rights against firms by directly interacting with them. But data brokers, firms that buy and sell data for consumers whom they do not directly interact with, are key players in the data economy. How is a consumer meant to exercise their rights against a broker with an “interaction gap” between them?

A handful of states have tried to soften the interaction gap by enacting data-broker-specific legislation under the “transparency model.” These laws, among other things, require brokers to publicly disclose themselves in state registries. The theory is that consumers would exercise their rights against brokers if they knew of the brokers’ existence. California recently went further with the Delete Act, providing consumers data-broker-specific privacy rights.

Assembling brokers’ reported privacy request metrics, this Comment performs an empirical analysis of the transparency model’s efficacy. These findings demonstrate that the transparency model does not effectively facilitate consumers in following through on their expected privacy preferences or meaningfully impacting brokers. Therefore, regulators should follow in the footsteps of the Delete Act and move beyond the transparency model.

Contemporary regulation of new digital technologies by nation-states unfolds under a darkening shadow of geopolitical competition. Three recent monographs offer illuminating and complementary maps of these geopolitical conflicts. Folding together insights from all three books opens up a new, more perspicacious understanding of geopolitical dynamics. This perspective, informed by all three books under consideration here, suggests grounds for skepticism about the emergence of a deep regulatory equilibrium centered on the emerging slate of European laws. The area of overlap will be strictly limited to less important questions by growing bipolar geostrategic conflict between the United States and China. Ambitions for global regulatory convergence when it comes to new digital technology, therefore, should be modest.

This Essay focuses largely on structural responses to AI pricing in antitrust, outlining the bulk of its argument in the context of merger law but also considers monopolization law and exclusionary conduct. It argues that the relationship between the strictness of the law and the sophistication of AI pricing is not straightforward. In the short run, a stricter approach to merger review might well make sense, but as AI pricing becomes more sophisticated, merger policy ought to become less strict: if anticompetitive outcomes are inevitable with or without a merger because of highly sophisticated AI pricing, antitrust interventions to stop mergers will not affect pricing and instead will create social losses by impeding efficient acquisitions. This Essay considers similar questions in the context of monopolization. It concludes by observing that the rise of AI pricing will strengthen the case for antitrust law to shift its focus away from high prices and static allocative inefficiency and toward innovation and dynamic efficiency.

A recurrent problem in adapting law to artificial intelligence (AI) programs is how the law should regulate the use of entities that lack intentions. Many areas of the law, including freedom of speech, copyright, and criminal law, make liability turn on whether the actor who causes harm (or creates a risk of harm) has a certain intention or mens rea. But AI agents—at least the ones we currently have—do not have intentions in the way that humans do. If liability turns on intention, that might immunize the use of AI programs from liability. We think that the best solution is to employ objective standards that are familiar in many different parts of the law. These legal standards either ascribe intention to actors or hold them to objective standards of conduct.

This Essay explores whether the use of AI to enhance decision-making brings about radical change in legal doctrine or, by contrast, is just another new tool. It focuses on decision-making by board members. This provides an especially relevant example because corporate law has laid out explicit expectations for how board members must go about decision-making.

This paper examines the distinct features of artificial intelligence (AI) and reaches a broader conclusion as to the availability and applicability of first-order tort rules. It evaluates the accuracy of the argument that AI is similar in essence to other emerging technologies that have entered our lives since the First Industrial Revolution and, therefore, does not require special legal treatment. The paper will explore whether our current tort doctrines can serve us well even when addressing AI liability.

Changing technologies render tax law’s intricacy legible in new ways. Advances in large language models, natural language processing, and programming languages designed for the domain of tax law make formalizations, or “representation[s] of [ ] legislation in symbols[ ] using logical connectives,” of tax law that capture much of its substance and structure both possible and realistic. These new formalizations can be used for many different purposes—what one might call flexible formalizations. Flexible formalizations will make law subject to computational analysis, including creating automated explanations of the analysis and testing statutes for consistency and unintended outcomes. This Essay builds upon existing work in computational law and digitalizing legislation.

The Facebook Oversight Board (the “FOB”) will see you now—well, at least a very small number of a select subset of you.