The accuracy, fairness, and limits of predicting recidivism

Author:

Dressel Julia1ORCID,Farid Hany1ORCID

Affiliation:

1. 6211 Sudikoff Laboratory, Department of Computer Science, Dartmouth College, Hanover, NH 03755, USA.

Abstract

Should we trust computers to make life-altering decisions in the criminal justice system?

Publisher

American Association for the Advancement of Science (AAAS)

Subject

Multidisciplinary

Reference19 articles.

1. W. L. Perry B. McInnis C. C. Price S. C. Smith J. S. Hollywood Predictive Policing: The Role of Crime Forecasting in Law Enforcement Operations (Rand Corporation 2013).

2. J. Angwin J. Larson S. Mattu L. Kirchner “Machine bias: There’s software used across the country to predict future criminals. And it’s biased against blacks ” ProPublica 23 May 2016; www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing.

3. W. Dieterich C. Mendoza T. Brennan “COMPAS risk scales: Demonstrating accuracy equity and predictive parity” (Technical Report Northpointe Inc. 2016).

4. False positives, false negatives, and false analyses: A rejoinder to “Machine bias: There’s software used across the country to predict future criminals. And it’s biased against blacks.”;Flores A. W.;Fed. Prob.,2016

5. J. Kleinberg S. Mullainathan M. Raghavan Inherent trade-offs in the fair determination of risk scores; https://arxiv.org/abs/1609.05807v2 (2016).

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