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The Challenges of Prediction: Lessons from Criminal Justice

I/S: A Journal of Law and Policy for the Information Society, Forthcoming

32 Pages Posted: 16 Oct 2017  

David G. Robinson

Georgetown University Law Center; Upturn

Date Written: October 16, 2017

Abstract

Government authorities at all levels increasingly rely on automated predictions, grounded in statistical patterns, to shape people’s lives. Software that wields government power deserves special attention, particularly when it uses historical data to decide automatically what ought to happen next.

In this article, I draw examples primarily from the domain of criminal justice — and in particular, the intersection of civil rights and criminal justice — to illustrate three structural challenges that can arise whenever law or public policy contemplates adopting predictive analytics as a tool:

1) What matters versus what the data measure;
2) Current goals versus historical patterns; and
3) Public authority versus private expertise.

After explaining each of these challenges and illustrating each with concrete examples, I describe feasible ways to avoid these problems and to do prediction more successfully.

Keywords: Predictive Analytics, Big Data, Criminal Law, Civil Rights

Suggested Citation

Robinson, David G., The Challenges of Prediction: Lessons from Criminal Justice (October 16, 2017). I/S: A Journal of Law and Policy for the Information Society, Forthcoming. Available at SSRN: https://ssrn.com/abstract=3054115

David Robinson (Contact Author)

Georgetown University Law Center ( email )

600 New Jersey Avenue, NW
Washington, DC 20001
United States

Upturn ( email )

1015 15th St, NW Suite 600
Washington, DC 20009
United States

HOME PAGE: http://teamupturn.com

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