Katyal on Democracy and Distrust in an Era of Artificial Intelligence

Sonia Katyal (UC Berkeley School of Law) has posted “Democracy and Distrust in an Era of Artificial Intelligence” (Daedalus, Journal of the American Academy of Arts & Sciences 2022) on SSRN. Here is the abstract:

Our legal system has historically operated under the general view that courts should defer to the legislature. There is one significant exception to this view: cases in which it appears that the political process has failed to recognize the rights or interests of minorities. This basic approach provides much of the foundational justifications for the role of judicial review in protecting minorities from discrimination by the legislature. Today, the rise of AI decision-making poses a similar challenge to democracy’s basic framework. As I argue in this essay, the rise of three trends–privatization, prediction, and automation in AI–have combined to pose similar risks to minorities. In this essay, I outline what a theory of judicial review would look like in an era of artificial intelligence, analyzing both the limitations and the possibilities of judicial review of AI. Here, I draw on cases in which AI decision-making has been challenged in courts, to show how concepts of due process and equal protection can be recuperated in a modern AI era, and even integrated into AI, to provide for better oversight and accountability.

Curagati on The Implementation of the Digital Markets Act with National Antitrust Laws

Christophe Carugati (Université Paris II) has posted “The Implementation of the Digital Markets Act with National Antitrust Laws” on SSRN. Here is the abstract:

The December 2020 Commission’s proposal for a Digital Markets Act (DMA) reached a compromised text with the Council and the Parliament on March 24, 2022. While the text that will impose obligations and prohibition rules on large online platforms acting as “gatekeepers” before any wrongdoing ex-ante is due to enter into force in October 2022, the same platforms are already under investigation in Germany under a DMA-like competition law that also imposes prohibition rules ex-ante. Other countries in Europe, including Italy, are considering following Germany and implementing new competition rules to adapt to the digital economy. How should the DMA implement with national competition laws? This question is crucial because inconsistency will inevitably hamper the effectiveness of both the DMA and national competition laws. The paper addresses this question by studying the DMA and German implementation framework. Section I explains how legislators envisage the implementation of the DMA with national competition laws. Section II then considers the implementation of the DMA-like national competition rules by focusing the analysis on Germany, which already enforced its new legislation in January 2022 against Google. Section III designs a cooperation model between the DMA and national competition laws. Section IV concludes.

Lu on Data Privacy, Human Rights, and Algorithmic Opacity

Sylvia Lu (UC Berkeley School of Law) has posted “Data Privacy, Human Rights, and Algorithmic Opacity” (California Law Review, Vol. 110, 2022) on SSRN. Here is the abstract:

Decades ago, it was difficult to imagine a reality in which artificial intelligence (AI) could penetrate every corner of our lives to monitor our innermost selves for commercial interests. Within a few decades, the private sector has seen a wild proliferation of AI systems, many of which are more powerful and penetrating than anticipated. In many cases, machine-learning-based AI systems have become “the power behind the throne,” tracking user activities and making fateful decisions through predictive analysis of personal information. However, machine-learning algorithms can be technically complex and legally claimed as trade secrets, creating an opacity that hinders oversight of AI systems. Accordingly, many AI-based services and products have been found to be invasive, manipulative, and biased, eroding privacy rules and human rights in modern society.

The emergence of advanced AI systems thus generates a deeper tension between algorithmic secrecy and data privacy. Yet, in today’s policy debate, algorithmic transparency in a privacy context is an issue that is equally important but managerially disregarded, commercially evasive, and legally unactualized. This Note illustrates how regulators should rethink strategies regarding transparency for privacy protection through the interplay of human rights, disclosure regulations, and whistleblowing systems. It discusses how machine-learning algorithms threaten privacy protection through algorithmic opacity, assesses the effectiveness of the EU’s response to privacy issues raised by opaque AI systems, demonstrates the GDPR’s inadequacy in addressing privacy issues caused by algorithmic opacity, and proposes new algorithmic transparency strategies toward privacy protection, along with a broad array of policy implications and suggested moves. The analytical results indicate that in a world where algorithmic opacity has become a strategic tool for firms to escape accountability, regulators in the EU, the US, and elsewhere should adopt a human-rights-based approach to impose a social transparency duty on firms deploying high-risk AI techniques.

Kaminski & Urban on The Right to Contest AI

Margot E. Kaminski (University of Colorado Law School; Yale ISP) and Jennifer M. Urban (UC Berkeley School of Law) have posted “The Right to Contest AI” (Columbia Law Review, Vol. 121, 2021) on SSRN. Here is the abstract:

Artificial intelligence (AI) is increasingly used to make important decisions, from university admissions selections to loan determinations to the distribution of COVID-19 vaccines. These uses of AI raise a host of concerns about discrimination, accuracy, fairness, and accountability.

In the United States, recent proposals for regulating AI focus largely on ex ante and systemic governance. This Article argues instead—or really, in addition—for an individual right to contest AI decisions, modeled on due process but adapted for the digital age. The European Union, in fact, recognizes such a right, and a growing number of institutions around the world now call for its establishment. This Article argues that despite considerable differences between the United States and other countries,establishing the right to contest AI decisions here would be in keeping with a long tradition of due process theory.

This Article then fills a gap in the literature, establishing a theoretical scaffolding for discussing what a right to contest should look like in practice. This Article establishes four contestation archetypes that should serve as the bases of discussions of contestation both for the right to contest AI and in other policy contexts. The contestation archetypes vary along two axes: from contestation rules to standards and from emphasizing procedure to establishing substantive rights. This Article then discusses four processes that illustrate these archetypes in practice, including the first in depth consideration of the GDPR’s right to contestation for a U.S. audience. Finally, this Article integrates findings from these investigations to develop normative and practical guidance for establishing a right to contest AI.