Datafication and the practice of intelligence production

Abstract

Datafication of social life affects what society regards as knowledge. Jasanoff’s regimes of sight framework provides three ideal-type models of authorised knowing in environmental data practice. This paper applies Jasanoff's framework for analysing intelligence practice through an exploratory empirical study of crime and intelligence practitioners in a selection of police services in Australia, New Zealand, Canada and the United States. The paper argues that the ‘view from somewhere’ (VFS) captures the essence of existing police intelligence practices in the four countries but the ‘view from nowhere’ (VFN) is emerging as a possible future for police intelligence – an approach promoted by technology companies and supported mainly by police leaders and managers. The paper investigates the challenges and limits of a shift by police from VFS to VFN in the production of intelligence; the challenges are primarily political, which threaten the dominance of police contextual knowledge over ‘scientific’ knowledge. These political challenges also have symbolic and material implications. The paper concludes that, because of these challenges, a complete shift from VFS to VFN is not likely to happen. At best the two models might co-exist with the latter subordinate to the imperatives of the former, resulting in further tension between sworn officers and civilians, organisational inertia, as well as technologies that may be under-utilised or abandoned.

The views about Big Data among professionals of police forces: A scoping review of empirical studies

“…The study by Neiva and colleagues (2022) also found that police professionals’ cooperation in the EU showed promising expectations about the role of Big Data in criminal investigations by making use of DNA databases; namely, in terms of its perceived capability to advance cold criminal cases and strengthen the interoperability of multiple data sets in ways that produce intelligence valuable for criminal investigations. Chan and colleagues (2022) draw similar conclusions: the interoperability of databases enhanced by the integration of technological platforms was conceived by the police professionals as an upgrade to their work. Participants’ views were focused on the benefits of Big Data in improving their intelligence work given its capabilities to enhance access, storage, analysis and sharing of different data.…”

“…The studies were published between 2015 (Sanders et al, 2015) and 2022 (Chan et al, 2022; Neiva et al, 2022; Simmler et al, 2022). Ten of the fourteen studies were conducted in a single country, namely the United Kingdom (Dencik et al, 2018; Fussey and Sandhu, 2020; Sandhu and Fussey, 2021), Canada (Sanders et al, 2015; Sanders and Condon, 2017), the United States (Brayne, 2017; Brayne and Christin, 2020), Colombia (Barreneche, 2019), Australia (Chan and Moses, 2017), and Switzerland (Simmler et al, 2022).…”

“…Ten of the fourteen studies were conducted in a single country, namely the United Kingdom (Dencik et al, 2018; Fussey and Sandhu, 2020; Sandhu and Fussey, 2021), Canada (Sanders et al, 2015; Sanders and Condon, 2017), the United States (Brayne, 2017; Brayne and Christin, 2020), Colombia (Barreneche, 2019), Australia (Chan and Moses, 2017), and Switzerland (Simmler et al, 2022). The others four studies derived from several countries: sixteen European Union (EU) countries (Spain, Poland, France, Czech Republic, Austria, Hungary, Slovenia, Sweden, Bulgaria, Italy, Finland, Cyprus, Malta, Germany, Lithuania and Latvia) (Neiva et al, 2022); Norway, Germany and Switzerland (Kaufmann et al, 2019); Germany and Switzerland (Egbert and Krasmann, 2020); Australia, New Zealand, Canada, and the United States (Chan et al, 2022).…”

“…They argued that the utilization of Big Data is shaped by historical policing norms and values (Sanders and Condon, 2017), and current Big Data analysis applications in predictive policing echo classical policing strategies based on past crime patterns (Egbert and Krasmann, 2020). Other participants mentioned that any significant changes occurred in work practices with Big Data (Chan et al, 2022; Chan and Moses, 2017; Sanders and Condon, 2017; Sandhu and Fussey, 2021), basing their perceptions on three main arguments: first, the belief that they do not have enough analytical knowledge on this topic (Chan and Moses, 2017; Sanders and Condon, 2017; Sandhu and Fussey 2021); second, officers’ awareness of the limitations of predictive policing technologies sustained their “reluctance” to use them (Sandhu and Fussey, 2021: 66); and third, perceptions that Big Data is not used and/or is not well integrated by their professional organization (Chan et al, 2022; Chan and Moses, 2017; Sanders and Condon, 2017; Sandhu and Fussey, 2021). Further, the interviewees involved in the research undertaken by Brayne and Christin (2020) perceived the implementation of Big Data as an answer to technical and political pressures to use the latest technological innovations in policing in order to maintain legitimacy among the population, rather than an improvement in their work practices because, in their view, the implementation of Big Data has no practical effects on policing.…”

“…Lastly, practical barriers were also pointed out as elements of concern by police professionals involved in some of the studies, in particular: (a) scarce economic resources for implementation of the technologies, and the non-interoperability with different databases (Chan et al, 2022; Chan and Moses, 2017; Sanders and Condon, 2017); (b) a lack of professional training for police officers (Chan and Moses, 2017; Sanders and Condon, 2017); and (c) conflict and political tension between analysts and police managers, as well as difficulties in accessing data and resources (Chan et al, 2022).…”