Economics research seminar: Ranae Jabri, University of Sydney

Ranae Jabri, University of Sydney

Topic

Predictive Power at What Cost? Economic and Racial Justice of Data-driven Algorithms

abstract

This paper studies how algorithms use variables to maximize predictive power at the cost of group equity. While algorithms may not explicitly include race or economic class, data variables that only marginally improve predictions may be included without consideration of how the inclusion of such variables exacerbates disparities in risk scores across race and economic status. I develop a framework to examine a recidivism risk assessment tool using risk score and novel pretrial defendant case data from 2013-2016 in Broward County, Florida. I find that defendants’ neighborhood data negligibly improve predictive power, but substantially widen disparities in defendant risk scores and false positive rates across race and economic status. Higher risk scores may lead to longer pretrial incarceration and downstream consequences, by impacting labor market outcomes. These findings underscore that machine learning objectives tuned to maximize predictive power can be in conflict with racial and economic justice.