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68 Crime L. & Soc. Change 1 (2017)

handle is hein.journals/crmlsc68 and id is 1 raw text is: Crime Law Soc Change (2017) 68:1-15 CrossMark DOI 10.1007/s10611-016-9678-7 Policing, crime and 'big data'; towards a critique of the moral economy of stochastic governance Carrie B. Sanders' - James Sheptycki2 Published online: 3 January 2017 O The Author(s) 2017. This article is published with open access at Springerlink.com Abstract The paper defines 'stochastic governance' as the governance of populations and territory by reference to the statistical representations of metadata. Stochastic governance aims at achieving social order through algorithmic calculation made actionable through policing and regulatory means. Stochastic governance aims to improve the efficiency and sustainability of populations and territory while reducing costs and resource consumption. The algorithmic administration of populations and territory has recourse to 'Big Data'. The big claim of Big Data is that it will revolu- tionize the governance of big cities and that, since stochastic governance is data driven, evidence-led and algorithmically analysed, it is based on morally neutral technology. The paper defines moral economy - understood to be the production, distribution, circulation and use of moral sentiments emotions and values, norms and obligations in social space - through which it advances a contribution to the critique of stochastic governance. In essence the argument is that certain technological developments in relation to policing, regulation, law and governance are taking place in the context of a neo-liberal moral economy that is shaping the social outcomes of stochastic gover- nance. Thinking about policing in both the narrow sense of crime fighting and more broadly in its Foucaldian sense as governance, empirical manifestations of 'policing with Big Data' exhibit the hallmarks of the moral economy of neo-liberalism. This suggests that a hardening of the socio-legal and technical structures of stochastic governance has already largely taken place. James Sheptycki contributions to this paper are based on research undertaken while holding SSHRC Insight Grant No. 435-2013-1283. W James Sheptycki jshep@yorku.ca Carrie B. Sanders csanders@wlu.ca Wilfrid Laurier University, Brantford, ON, Canada 2 York University, Toronto M3J 1P3, Canada 4_ Springer

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