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AbstractPredictive policing is among the most prevalent new technological tools for law enforcement. Understanding how the police produce knowledge about crime and society in technologically mediated ways is important vis‐a‐vis practices of social ordering. In this paper, I suggest to draw on literature from Science and Technology Studies (STS) in order to understand the impact of technology not as analytically isolated artefact, but as embedded in socio‐technical relations that define how it comes to matter in everyday practice. In order to demonstrate what it means to think about technology in socio‐technical terms, I engage the discrepancy between the technical capacities of predictive policing applications and the ways in which they actually become part of police work on a daily basis. Specifically, I investigate how claims about the automation of crime analysis in predictive policing are reconfigured through the interplay of social and technical elements in police work.

Das letzte Jahrzehnt hat einige technologische Quantensprünge mit sich gebracht, die sowohl die internationale Politik als auch deren wissenschaftliche Rezeption geprägt haben. Dieser Beitrag gibt einen Überblick über die aktuellen Debatten in Bezug auf die analytische Einordnung von Technologie in den Internationalen Beziehungen (IB) und identifiziert drei größere, zusammenhängende Trends. Der erste Trend kritisiert den vorherrschenden Technik-Determinismus und die analytische Externalisierung von Technologie als Variable in den IB. Der zweite Trend drückt sich in einer zunehmenden konzeptionellen Hinwendung zu technik- und wissenschaftssoziologischen Perspektiven aus. Der dritte Trend beinhaltet eine teilweise methodologische Rekalibrierung hin zu qualitativ-empirischer Feldforschung.

AbstractThis article engages the EU's approach to science diplomacy through the case of the Swiss 'mass migration initiative' in 2014. The legislative proposal, legally binding through its adoption by Swiss voters, obliged the Swiss government to take sovereign control over migration management, thereby preventing the ratification of the 'Croatia Protocol' that would have, through the EU regime of free movement of persons and the bilateral treaties between Switzerland and the EU, granted Croatian citizens free movement to Switzerland. As the refusal to ratify the protocol effectively undercut the bilateral treaties, the EU Commission in turn used the Swiss participation in the European research funding framework Horizon 2020 as leverage to enforce Swiss compliance to the bilateral treaties. This eventually compelled the Swiss government to ratify the Croatia Protocol despite the domestic legal clash with the 'mass migration initiative' proposal. Conceptually, the article turns the attention to contradicting policy goals in an emerging EU science diplomacy framework, and shows how science diplomacy in the conflict around the Croatia Protocol got stuck between a liberal 'carrot' and a realist 'stick'.

The European Union's (EU) external border framework is not only increasingly reliant on digital databases, but these databases are now set to become interoperable. By 2020, the European Commission (EC) aims to have a fully interconnected new architecture for identity management at the border in place. Based on biometric enrolment of all third-country citizens, Europe's new digital borders raise a number of concerns, including suspicion, large-scale surveillance, and internal policing that spread well beyond the border site. Border management today is embedded into a complex network of data collection and data analysis that provides authorities with knowledge about who (or what) attempts to cross the border. While still serving as physical chokepoints for the examination and extraction of dangerous, suspicious, or illegitimate elements from global flows of mobility, border operations therefore increasingly rely on a number of databases.

This article argues that with increasingly large databases and computational power, profiling as a key part of security governance is experiencing major changes. Targeting mobile populations in order to enact security via controlling and sifting the good from the bad, profiling techniques accumulate and process personal data. However, as advanced algorithmic analytics enable authorities to make sense of unprecedented amounts of information and derive patterns in a data-driven fashion, the procedures that bring risk into being increasingly differ from those of traditional profiling. While several scholars have dealt with the consequences of black-boxed and invisible algorithmic analytics in terms of privacy and data protection, this article engages the effects of knowledge-generating algorithms on anti-discriminatory safeguards. Using the European-level efforts for the establishment of a Passenger Name Record (PNR) system as an example, and on the theoretical level connecting distinct modes of profiling with Foucauldian thought on governing, the article finds that with pattern-based categorizations in data-driven profiling, safeguards such as the Charter of Fundamental Rights of the European Union or the EU data-protection framework essentially lose their applicability, leading to a diminishing role of the tools of the anti-discrimination framework.

This book explores how predictive policing transforms police work. Police departments around the world have started to use data-driven applications to produce crime forecasts and intervene into the future through targeted prevention measures. Based on three years of field research in Germany and Switzerland, this book provides a theoretically sophisticated and empirically detailed account of how the police produce and act upon criminal futures as part of their everyday work practices. The authors argue that predictive policing must not be analyzed as an isolated technological artifact, but as part of a larger sociotechnical system that is embedded in organizational structures and occupational cultures. The book highlights how, for crime prediction software to come to matter and play a role in more efficient and targeted police work, several translation processes are needed to align human and nonhuman actors across different divisions of police work. Police work is a key function for the production and maintenance of public order, but it can also discriminate, exclude, and violate civil liberties and human rights. When criminal futures come into being in the form of algorithmically produced risk estimates, this can have wide-ranging consequences. Building on empirical findings, the book presents a number of practical recommendations for the prudent use of algorithmic analysis tools in police work that will speak to the protection of civil liberties and human rights as much as they will speak to the professional needs of police organizations. An accessible and compelling read, this book will appeal to students and scholars of criminology, sociology, and cultural studies as well as to police practitioners and civil liberties advocates, in addition to all those who are interested in how to implement reasonable forms of data-driven policing.