Privacy’s Political Economy and the State of Machine Learning

26 Pages Posted: 15 May 2019 Last revised: 20 Jul 2021

See all articles by Mariano-Florentino Cuéllar

Mariano-Florentino Cuéllar

Carnegie Endowment for International Peace; Stanford Law School

Aziz Z. Huq

University of Chicago - Law School

Date Written: May 9, 2019


Our aim in this essay is to consider how policymakers make decisions about government surveillance 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. In order to lay the groundwork for more nuanced engagement with the legal and policy trade-offs in this domain, we focus particularly on how the use of privacy-relevant technologies is affected by the machine-learning state’s political economy –– the mix of agendas, pressures, and constraints affecting institutions and the policymaking process.

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

Cuéllar, Mariano-Florentino and Huq, Aziz Z., 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:

Mariano-Florentino Cuéllar

Carnegie Endowment for International Peace ( email )

1779 Massachuesetts Avenue, N.W.
Washington, DC 20036
United States

Stanford Law School ( email )

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

Aziz Z. Huq (Contact Author)

University of Chicago - Law School ( email )

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

Do you have negative results from your research you’d like to share?

Paper statistics

Abstract Views
PlumX Metrics