Data pollution

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.