STS and Computational Knowledge Production in Policing and Criminal Justice - II

Abstract

Recent innovations in technologies and processes of data analysis and computational science – mainly in reference to terms (and myths) like big data, algorithmic decision making and artificial intelligence – have transformed many processes of knowledge production in the fields of policing and criminal justice. With predictive policing as one of its earliest and most prominent representatives (Perry et al. 2013; Bennett Moses & Chan, 2018), the algorithmic mediated production of (prospective) knowledge has now also affected the criminal justice system at every level. Different predictive models include generating risky spaces – PredPol (Mohler et al., 2015); risky individuals – Chicago’s ‘strategic subject list’ (Saunders/Hunt/Hollywood, 2016) and US’ Computer-Assisted Passenger Pre-screening System; or calculating the recidivism risk of convicted offenders in order to inform the sentence decision (predictive prosecution) (Ferguson, 2016). This development has gone hand-in-hand with a rapid technological expansion from the frontline to the back office. Thus, regardless of whether suspects or spaces are objects of (predictive) knowledge production, or if recidivism risk scores for convicted offenders are generated, in the end, policing and justice are increasingly characterized by socio-technical interwovenness with digital data production and algorithmic technologies. This calls for a thorough STS analysis to get at the innovations, interruptions, regenerations, successes and failures herein involved in the co-construction of policing practices and technological development. Correspondingly, this panel seeks to ask how STS can provide analytical tools for grasping the entanglement of technology and society involved in the development and implementation of computational knowledge production in policing and criminal justice.

Chairs

• Simon Egbert, Technische Universität Berlin

• Nikolaus Pöchhacker, MCTS, Technical University of Munich

• Thomas Linder, Queen's University, Kingston, Canada

Individual Presentations

• Interfacing Predictive Policing: Objectivity and Expertise Between Police and Data Scientists - Zoe Carey, New School for Social Research

• The Science of “Civil Unrest Prediction” Meets Social Media - Gabriel Grill, University of Michigan; Christian Sandvig, University of Michigan

• Using Big Data Analytics to Tackle Money Laundry in Nigerian Governmental Organizations: Prospects and Challenges - Rufai Rahim Abubakar, National Centre for Technology Management

• Analysing Predictive Policing in a Rawlsian Framework - Maitreyi Menon, FLAME University

Session Organizer

• Jens Hälterlein, Long Distance University Hagen