Privacy’s Political Economy and the State of Machine Learning

23 Pages Posted: 15 May 2019 Last revised: 2 Aug 2019

See all articles by Aziz Z. Huq

Aziz Z. Huq

University of Chicago - Law School

Mariano-Florentino Cuéllar

Stanford Law School; Freeman Spogli Institute for International Studies

Date Written: May 9, 2019

Abstract

Our aim in this essay is to offer a preliminary sketch of the basic political economy of privacy in what we might call the machine-learning state –– a nation state with sufficient bureaucratic and technological capacity to rely extensively on machine learning techniques for surveillance, enforcement, and security. We focus particularly on the question of how the machine-learning state’s political economy influences a state’s decision to adopt privacy-relevant technologies. Since machine learning tools can be deployed in many ways that are not pertinent to privacy, our focus is on a specific subset of state uses of such technology –– to engage in surveillance of the public and its activities. In order to lay the groundwork for more nuanced engagement with the legal and policy trade-offs in this domain, we map the main technological and institutional forces shaping a state’s deployment of new machine learning capabilities that can affect privacy.

We propose that a nation state’s adoption of machine learning instruments for surveillance occurs in the context of what Robert Putnam famously characterized as a “two-level game.” The state is operating simultaneously in a domestic political environment populated by institutions mediating conflicts involving civil society and firms competing to expand and monetize machine-learning capacities, and also in an international environment in which it is competing with other sovereign nations that are cultivating and deploying similar capacities for geostrategic ends. How and to what end machine learning instruments are deployed depends on the strategic choices the national government makes in these two overlapping yet distinct contexts. It would thus be a mistake to analyze such deployment purely in terms of responses to domestic legal, policy, or political considerations, because states may be willing to shoulder the costs of domestic backlash so they can further geostrategic goals. We elucidate the pressures likely to affect legal and policy interventions in this space, and in the process, map some of the concerns and trade-offs relevant to the reasonable evaluation of possible reforms. We consider new directions for privacy-related scholarly research. Finally, we discuss the limitations of existing Fourth Amendment doctrine as a means of addressing the situation we describe, and the potential for state and federal legislative or regulatory alternatives to fill the gap.

Keywords: privacy; machine learning; political economy; constitutional rights; geopolitics

Suggested Citation

Huq, Aziz Z. and Cuéllar, Mariano-Florentino, Privacy’s Political Economy and the State of Machine Learning (May 9, 2019). NYU Annual Survey of American Law, Forthcoming; U of Chicago, Public Law Working Paper No. 714. Available at SSRN: https://ssrn.com/abstract=3385594

Aziz Z. Huq (Contact Author)

University of Chicago - Law School ( email )

1111 E. 60th St.
Chicago, IL 60637
United States

Mariano-Florentino Cuéllar

Stanford Law School ( email )

559 Nathan Abbott Way
Stanford, CA 94305-8610
United States
650-723-9216 (Phone)
650-725-0253 (Fax)

Freeman Spogli Institute for International Studies ( email )

United States

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