2023-04-04 15:30 - 17:00 E19.14
Fieke will present a case study of "Top400", a predictive identification program in Amsterdam, where a home grown data model is used to identify 400 youngsters that are at risk of engaging in a criminal career.
The advent of predictive policing systems has been a key object of study to theorize about new forms of algorithmic governance and its impact on society. With the term predictive policing systems I refer to tools that uses data sets of different sizes to feed into an algorithmic model that is intended to predict either places where crime is most likely to occur in the near future (hotspot policing), or persons who are likely to get involved in crime (predictive identification). These tools come with the promise of doing more with less resources, as it should allow the police to better allocate resources, adjusting the patrol frequency in ‘risky’ neighbourhoods, which will lead to crime reduction (Van Brakel, 2016; Brayne, 2017, 2020; Hardyns and Rummens, 2018). Scholarly critiques on the turn to predictive policing raise concerns about the reliance on datasets that reflect historic inequalities and perpetuate racialised policing (Williams and Clarke, 2018). These systems create policing futures in which police attention is increasingly directed to already over-policed communities, as these tools do not analyse nor predict crime but analyse and predict police activity (Lum and Isaac, 2016). What is missing from these debates is the political and institutional drivers that are shaping why and how police are turning to predictive policing systems, which in turn shape how these data models are constructed. This paper will discuss the case study of the Top 400, a predictive identification program that is hosted by the city of Amsterdam in partnership with a number of public authorities including the Amsterdam police department. Here a home grown data model is used to identify 400 youngsters that are at risk of engaging in a criminal career. The research is based on an analysis of over 2500 pages of FOI documents provided by the municipality of Amsterdam and the Amsterdam police. These offer insights into the ambiguities that materialize when abstract political problems end up criminalize nuisance behaviour, when care is instrumentalised for crime prevention, and the challenges of test statistical models in the wild.
Bio: Dr Fieke Jansen is a postdoctoral researcher at the University of Amsterdam and co-principle investigator of the critical infrastructure lab. She did her PhD at the Data Justice Lab at Cardiff University where she looked at the institutional and societal implications of the introduction of predictive identification and biometric recognition in Belgium, the Netherlands, and the UK. She is the author of the mapping study ‘Data driven policing in the context of Europe’ and co-author of ‘Biometric identity systems in law enforcement and the politics of (voice) recognition: The case of SiiP’ (alongside Lina Dencik and Javi Sánchez-Monedero). Throughout her work Fieke worked to repoliticize and decenter technology in discussions around harms, justice and rights. Fieke is the Chair of the board of the Digital Freedom Fund. Prior to starting her PhD, Fieke worked at Tactical Tech, a Berlin based NGO, as the project lead for their Politics of Data programme. At the Dutch Development organisation, Hivos, the intersection of human rights, internet and freedom of expression.