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Risk, Intersectional Inequalities and Racial Proxies: How Is Machine Learni...

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Centre for Ethics

Room 200, Second Floor, Larkin Building, 15 Devonshire Place

Toronto, ON, Ontario M5S 1H8

Canada

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Risk, Intersectional Inequalities and Racial Proxies: How Is Machine Learning and Big Data Shaping Legal and Criminal Justice Analysis of “Risk”?

CJS and social justice organizations and individuals are challenging and redefining conventional risk episteme(s) through the use of big data analytics, which are shifting organizational risk practices, challenging social science methods of assessing risk, and affecting knowledge about risk. I argue that big data reconfigures risk by producing a form of algorithmic risk, which is different from the actuarial risk techniques already in use in many justice sectors; that new experts are entering the risk game: technologists who make data public and accessible to a range of stakeholders; and that big data analytics can be used to produce forms of usable knowledge but questions still persist on whether or not these technologies can learn how to limit bias and inequality.

Kelly Hannah-Moffat
University of Toronto
Criminology & Sociolegal Studies

04:00 PM - 06:00 PM
Centre for Ethics, University of Toronto
200 Larkin

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Centre for Ethics

Room 200, Second Floor, Larkin Building, 15 Devonshire Place

Toronto, ON, Ontario M5S 1H8

Canada

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