Technology

The central concern of structural constitutional law is the organization of governmental power, but power comes in many forms. This Article develops an original account of data’s structural law—the processes, institutional arrangements, transparency rules, and control mechanisms that, we argue, create distinctive structural dynamics for data’s acquisition and appropriation to public projects. Doing so requires us to reconsider how law treats the category of power to which data belongs. Data is an instrument of power. The Constitution facilitates popular control over material forms of power through distinctive strategies, ranging from defaults to accounting mechanisms. Assessing data’s structural ecosystem against that backdrop allows us to both map the structural law of data and provide an initial diagnosis of its deficits. Drawing on our respective fields—law and computer science—we conclude by suggesting legal and technical pathways to asserting greater procedural, institutional, and popular control over the government’s data.

Critics of generative AI often describe it as a “plagiarism machine.” They may be right, though not in the sense they mean. With rare exceptions, generative AI doesn’t just copy someone else’s creative expression, producing outputs that infringe copyright. But it does get its ideas from somewhere. And it’s quite bad at identifying the source of those ideas. That means that students (and professors, and lawyers, and journalists) who use AI to produce their work generally aren’t engaged in copyright infringement. But they are often passing someone else’s work off as their own, whether or not they know it. While plagiarism is a problem in academic work generally, AI makes it much worse because authors who use AI may be unknowingly taking the ideas and words of someone else.

Disclosing that the authors used AI isn’t a sufficient solution to the problem because the people whose ideas are being used don’t get credit for those ideas. Whether or not a declaration that “AI came up with my ideas” is plagiarism, failing to make a good-faith effort to find the underlying sources is a bad academic practice.

We argue that AI plagiarism isn’t—and shouldn’t be—illegal. But it is still a problem in many contexts, particularly academic work, where proper credit is an essential part of the ecosystem. We suggest best practices to align academic and other writing with good scholarly norms in the AI environment.

Beware dark patterns. The name should be a warning, perhaps alluding to the dark web, the “Dark Lord” Sauron, or another archetypically villainous and dangerous entity. Rightfully included in this nefarious bunch, dark patterns are software interfaces that manipulate users into doing things they would not normally do. Because of these First Amendment complications, the constitutionality of dark pattern restrictions is an unsettled question. To begin constructing an answer, we must look at how dark patterns are regulated today, how companies have begun to challenge the constitutionality of such regulations, and where dark patterns fall in the grand scheme of free speech. Taken together, these steps inform an approach to regulation going forward.

Holders of patents covering technology standards, known as standard-essential patents (SEP), control the rights to an invention with no commercially-viable alternative or that cannot be designed around while still complying with a standard. This gives SEP holders significant leverage in licensing negotiations. Standards development organizations (SDOs) play an important role in curbing opportunistic behavior by patent holders. SDOs require SEP holders to license their patents on fair, reasonable, and non-discriminatory (FRAND) terms. However, courts have mischaracterized FRAND commitments, concluding that these disputes carry a Seventh Amendment guarantee to a jury trial. This mischaracterization undermines the fair resolution of FRAND disputes, and a different approach is necessary. In this Comment, Marta Krason proposes an alternative analytical framework that more accurately characterizes FRAND disputes by drawing on principles from contract and property law, concluding that the constitutionally proper adjudicator is a judge, not a jury.

The internet plays a crucial role in modern life; however, equal access to it is not guaranteed. Drawing on existing tribal spectrum sovereignty arguments, Morgan Schaack writes that the control exercised by the FCC’s licensing of the electromagnetic spectrum and language common in many tribal treaties create a tribal access right to spectrum under the trust responsibility. Framing this access to spectrum as a trust-protected resource, the Comment situates allowing tiered internet service in the absence of net neutrality as a violation of the government's obligations under the trust responsibility.

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.