ABSTRACT
The chapter presents the defining challenges related to big data, algorithms, artificial intelligence and predictive analytics in crime and social control. It focuses on the semantic novelties brought by big data and algorithmic analytics in this domain, where the new language of mathematics is used for blurring contemporary regulatory boundaries, undercutting the safeguards built into regulatory regimes, and abolishing subjectivity and case-specific narratives. It continues by tackling the knowledge-production changes provoked by big data and the “fourth scientific revolution” big data has supposedly triggered. The epistemological novelties of big data have been encapsulated in various formulations, such as “the numbers speak for themselves”, “we do not know the questions, but we can provide the answers”, etc., and this has led to profound changes in power relations, which have shifted to those who can afford to engage data scientists in order to achieve their ends. The chapter then traces the origins of big data in industry and looks at how the underlying assumptions, such as “doing more with less”, are being transferred to other domains where these assumptions and aspirations from industry have negative consequences for fundamental liberties and social justice. Then it shows changes in the governance model in several domains, and traces and analyses several deficiencies of big data and algorithmic justice, such as the risk of de-identification due to increasingly blurring lines between personal and impersonal data; the needle-in-a-haystack problem and the ad infinitum hurdle; self-fulfilling prophecies and the vicious circle effect of predictive analytics; the blurring lines between probability, causality and certainty; “dirty data” and “runaway algorithms”; and risks to the democratic political system. It concludes by explaining the common ground of the chapters of the book and by presenting the structure of the book and some insights regarding the other chapters thereof.
