Many more examples could and should be discussed. Finally, we discuss all four proposals in the context of different test cases: social media, student education software and credit and cell phone markets. Throughout these different strategies, we show how salience of data collection can be coupled with attempts to prevent discrimination and exploitation of users. 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. Finally, we trace back new Big Data personalization techniques to the old Hartian precept of treating like cases alike and different cases – differently. Third, we suggest democratizing data collection by regular user surveys and data compliance officers partially elected by users. Second, we propose using the doctrine of unconscionability to prevent contracts that unreasonably favor data-collecting companies. 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. Four instruments stand out: First, active choice may be mandated between data collecting-services (paid by data) and data-free services (paid by money). To rein in Big Data's potential, we adapt regulatory strategies from behavioral economics, contracts and criminal law theory. Anti-discrimination law and transparency alone, however, cannot do the job of curbing Big Data's negative externalities while fostering its positive effects. The growing differentiation of services based on Big Data harbors the potential for both greater societal inequality and for greater equality.
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