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      Algorithmic risk governance: Big data analytics, race and information activism in criminal justice debates

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      Theoretical Criminology
      SAGE Publications

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          Abstract

          Meanings of risk in criminal justice assessment continue to evolve, making it critical to understand how particular compositions of risk are mediated, resisted and re-configured by experts and practitioners. Criminal justice organizations are working with computer scientists, software engineers and private companies that are skilled in big data analytics to produce new ways of thinking about and managing risk. Little is known, however, about how criminal justice systems, social justice organizations and individuals are shaping, challenging and redefining conventional actuarial risk episteme(s) through the use of big data technologies. The use of such analytics is shifting organizational risk practices, challenging social science methods of assessing risk, producing new knowledge about risk and consequently new forms of algorithmic governance. This article explores how big data reconfigure risk by producing a new form of algorithmic risk—a form of risk which is posited as different from the social science (psychologically) informed risk techniques already in use in many justice sectors. It also shows that new experts are entering the risk game, including technologists who make data public and accessible to a range of stakeholders. Finally, it demonstrates that big data analytics can be used to produce forms of usable knowledge that constitute types of ‘information activism’. This form of activism produces alternative risk narratives, which are focused on ‘criminogenic structures’ or ‘criminogenic policy’.

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                Author and article information

                Journal
                Theoretical Criminology
                Theoretical Criminology
                SAGE Publications
                1362-4806
                1461-7439
                November 16 2018
                November 2019
                March 22 2018
                November 2019
                : 23
                : 4
                : 453-470
                Affiliations
                [1 ]University of Toronto, Ontario, Canada
                Article
                10.1177/1362480618763582
                02ff2ea0-0829-4140-b571-ad918daecd2d
                © 2019

                http://journals.sagepub.com/page/policies/text-and-data-mining-license

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