Document Type


Publication Date



The growing differentiation of services based on Big Data harbors the potential for both greater societal inequality and for greater equality. Anti-discrimination law and transparency alone, however, cannot do the job of curbing Big Data’s negative externalities while fostering its positive effects.

To rein in Big Data’s potential, we adapt regulatory strategies from behavioral economics, contracts and criminal law theory. Four instruments stand out: First, active choice may be mandated between data collecting-services (paid by data) and data-free services (paid by money). Our suggestion provides concrete estimates for the price range of a data-free option, sheds new light on the monetization of data-collecting services, and proposes an “inverse predatory pricing” instrument to limit excessive pricing of the data-free option. Second, we propose using the doctrine of unconscionability to prevent contracts that unreasonably favor data-collecting companies. Third, we suggest democratizing data collection by regular user surveys and data compliance officers partially elected by users. Finally, we trace back new Big Data personalization techniques to the old Hartian precept of treating like cases alike and different cases – differently. If it is true that a speeding ticket over $50 is less of a disutility for a millionaire than for a welfare recipient, the income and wealth-responsive fines powered by Big Data that we suggest offer a glimpse into the future of the mitigation of economic and legal inequality by personalized law. Throughout these different strategies, we show how salience of data collection can be coupled with attempts to prevent discrimination and exploitation of users. Finally, we discuss all four proposals in the context of different test cases: social media, student education software and credit and cell phone markets.

Many more examples could and should be discussed. In the face of increasing unease about the asymmetry of power between Big Data collectors and dispersed users, about differential legal treatment, and about the unprecedented dimensions of economic inequality, this paper proposes a new regulatory framework and research agenda to put the powerful engine of Big Data to the benefit of both the individual and societies adhering to basic notions of equality and non-discrimination.


This paper has benefited from comments by Chris Jay Hoofnagle, Markus Düttmann, Greg Kimak, and participants in the conference “Unlocking the Black Box: The Promise and Limits of Algorithmic Accountability in the Professions”, held on April 1-2 at the Yale Law School, the 2nd Berlin Center for Consumer Policy Forum, held on April 13 at the Berlin Science Center (WZB), as well as a M-EPLI talk at the Maastricht European Private Law Institute, held on April 26. All errors remain entirely our own.