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Activity Number: 83
Type: Invited
Date/Time: Sunday, July 30, 2017 : 8:30 PM to 10:30 PM
Sponsor: Social Statistics Section
Abstract #324519
Title: Fair Prediction with Disparate Impact: a Study of Bias in Recidivism Prediction Instruments
Author(s): Alexandra Chouldechova*
Companies: Carnegie Mellon University
Keywords: Risk assessment ; Prediction ; Fairness ; Ethics ; Disparate Impact ; Model assessment
Abstract:

Recidivism prediction instruments provide decision makers with an assessment of the likelihood that a criminal defendant will reoffend at a future point in time. While such instruments are gaining increasing popularity across the country, their use is attracting tremendous controversy. Much of the controversy concerns potential discriminatory bias in the risk assessments that are produced. This paper discusses a fairness criterion originating in the field of educational and psychological testing that has recently been applied to assess the fairness of recidivism prediction instruments. We demonstrate how adherence to the criterion may lead to considerable disparate impact when recidivism prevalence differs across groups.


Authors who are presenting talks have a * after their name.

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