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Data-driven policing: how digital technologies transform the practice and governance of policing

Marciniak, Daniel (2021) Data-driven policing: how digital technologies transform the practice and governance of policing. PhD thesis, University of Essex.

Data-driven policing - how digital technologies transform the practice and governance of policing (Daniel Marciniak).pdf

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Adoption of advanced digital technology is one of the most controversial and fundamental transformations in contemporary police practice. Despite its significance, empirical enquiry of these technologies, and the way they shape and are shaped by policing environments is rare. Drawing on in-depth interviews with officers in a UK police force and ethnographic fieldwork in a large metropolitan police department in the US, this thesis aims to make a significant empirical and conceptual contribution to this emerging field. It reveals the multifaceted character of data-driven technology in policing and the complex socio-technical negotiations that take place in these operational environments. In the UK case study, data visualisation and predictive policing techniques have been adopted to reduce service demand and prioritise actions in the context of austerity-driven budget cuts. In the US, following a federal consent decree addressing varied civil rights grievances, compliance monitoring requirements have brought about rapid datafication and digitisation of many policing tasks including a radical adaptation of existing CompStat processes. Although both police forces employ similar arsenals of digital tools, these fuel very different types of policing: Within a mind-set of crime-fighting, US applications emphasise targeted surveillance and territorial patrol. In the UK, officers seek a balance between enforcement and finding support through social services. Contrary to common techno-deterministic descriptions of predictive and digital policing, this research finds complex co-constructions of risk, suspicion, and priorities that are contingent on operational settings. In such settings, calculative and experiential knowledge intersect in discretionary decision-making. Yet the data reveals how technologies also have affordances: risk scores institutionalise a focus on repeat offenders, crime statistics drive competition between districts, and digital records shape the possibilities of policing. In other respects, the socio-technological relationships are often fragile: computer systems crash, display inaccurate information, and officers improvise workarounds. With increasing police reliance on production, processing, and interpretation of data, such insights demonstrate the complex ways police organisations become implicated in the outcomes of digital policing.

Item Type: Thesis (PhD)
Uncontrolled Keywords: accountability, AI, algorithms, big data, CompStat, crime prediction, discretion, ethnography, law enforcement, policing, policing innovation, predictive policing, surveillance, technology
Subjects: H Social Sciences > HM Sociology
Divisions: Faculty of Social Sciences > Sociology, Department of
Depositing User: Daniel Marciniak
Date Deposited: 06 Apr 2021 11:13
Last Modified: 06 Apr 2021 11:13

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