Omri Ben-Shahar’s research while affiliated with University of Chicago and other places

Regulatory and sociological resistance to new market-driven technologies, particularly to those that rely on collection and analysis of personal data, is prevalent even in cases where the technology creates large social value and saves lives. This article is a case study of such tragic technology resistance, focusing on tracking devices in cars which allow auto insurers to monitor how policyholders drive and adjust the premiums accordingly. Growing empirical work reveals that such “usage-based insurance” induces safer driving, reducing fatal accidents by almost one third, and resulting in more affordable and fair premiums. Yet, California prohibits this technology and other states limit its effectiveness, largely in the interest of privacy protection. The article evaluates the justifications fueling the restrictive regulation vis-à-vis the loss of lives resulting from this regulation. It concludes that the social benefits of the tracking technology dramatically outweigh the privacy and related costs.

Objective: to develop and substantiate the theory of data pollution, which makes it possible to realize and assess the harms the economy of big data creates. Methods: dialectical approach to cognition of social phenomena, allowing to analyze them in historical development and functioning in the context of the totality of objective and subjective factors, which predetermined the following research methods: formal-logical and sociological. Results: This article develops a novel framework – data pollution – to rethink the harms the data economy creates and the way they have to be regulated. The author argues that social intervention should focus on the external harms from collection and misuse of personal data. The article challenges the hegemony of the prevailing view that the injuries from digital data enterprise are exclusively private. That view has led lawmakers to focus solely on privacy protection as the regulatory objective. The article claims, instead, that a central problem in the digital economy has been largely ignored: how the information given by people affects others, and how it undermines and degrades public goods and interests. Scientific novelty: The data pollution concept offers a novel perspective why existing regulatory tools – torts, contracts, and disclosure law – are ineffective, mirroring their historical futility in curbing the harms from industrial pollution. The data pollution framework also opens up a rich roadmap for new regulatory devices – “an environmental law for data protection” – which focuses on controlling these external effects. The article examines how the tools used to control industrial pollution – production restrictions, carbon tax, and emissions liability – could be adapted to govern data pollution. Practical significance: the main provisions and conclusions of the article can be used in scientific, pedagogical and law enforcement activities when considering the issues related to the theory of data pollution.

This chapter examines how equality in the eyes of the law would survive if legal commands are personalized and result in different rules for different people. It argues that nothing in the framework of personalized law violates equality before the law. On the contrary, personalized treatment provides tools to distribute rights and burdens in a manner that conforms to egalitarian views and to notions of desert and need. If desert and need are determined by relevant attributes in a proportional manner, a just system should treat people differently . The chapter examines how personalized law, when designed to promote goals other than equality, could be bolstered (or constrained) by various notions of distributive justice. It recognizes that the use of Big Data and artificial intelligence could itself be a source of injustice, perpetuating historical biases. The chapter discusses ways to resolve this concern. Finally, it compares the deliberate differentiation of commands under personalized law with unintended forms of differential treatment pervasive under uniform laws. It concludes that the use of a multitude of relevant factors to personalize commands, derived from transparent statistical methods, offers novel opportunities to promote distributive justice goals under the law.

This chapter examines problems of coordination that could arise under personalized law, where different people are subject to different rules guiding their behavior. While earlier chapters of the book focused on individual actors and their personal legal environments, this chapter shifts to look at joint activity—how the interaction between people could be affected by personalized law. Would a personalized law regime that optimizes the atomistic parts result in more social harm, by neglecting the composite whole? It is often noted, correctly, that standardization and uniformity are needed for well-coordinated activity, to help people anticipate what others will do and synchronize their own actions. The chapter argues, however, that personalized law has a surprising potential to advance new forms of coordination. The chapter examines rules of traffic, methods of contracting, procedure in litigation, and the forms of property rights ( numerus clausus ), and argues that a properly designed personalized law regime could potentially achieve coordination in these spheres despite the greater variation and the non-uniformity of rules and of individual actions.

This chapter concludes the book by offering some preliminary reflections on the robotic aspects of personalized law. It begins by identifying some early experiments with the use of algorithms and machine learning in law, noting the immense potential they unveil. It confronts the “see” versus “scan” methodologies for individualized treatment—judges “looking people in the eye” versus algorithms analyzing the numerous personal aspects they are permitted to scan. The chapter highlights the critical roles of humans in algorithmic personalized law, primarily in setting the goals that the algorithms will be coded to optimize, in choosing the data by which algorithms are trained and people are subsequently screened, and in scrutinizing and repairing undesirable patterns. The chapter argues that the need to set specific goals and priorities for each law would transform the common law method of legal refinements, and would offer greater transparency for legislative accords. The book ends by pointing to areas of the law most ripe for phase-one personalized law.