Detecting Racial Bias in Jury Selection

TL;DR

This paper investigates racial bias in jury selection by applying advanced feature selection and classification methods, revealing significant evidence of bias and identifying specific subgroups affected.

Contribution

It introduces optimal feature selection and classification trees to improve detection and understanding of racial bias in jury strike decisions.

Findings

Significant racial bias found in strike decisions

Three juror subgroups exhibit notable racial disparities

Optimal methods outperform heuristic feature selection

Abstract

To support the 2019 U.S. Supreme Court case "Flowers v. Mississippi", APM Reports collated historical court records to assess whether the State exhibited a racial bias in striking potential jurors. This analysis used backward stepwise logistic regression to conclude that race was a significant factor, however this method for selecting relevant features is only a heuristic, and additionally cannot consider interactions between features. We apply Optimal Feature Selection to identify the globally-optimal subset of features and affirm that there is significant evidence of racial bias in the strike decisions. We also use Optimal Classification Trees to segment the juror population subgroups with similar characteristics and probability of being struck, and find that three of these subgroups exhibit significant racial disparity in strike rate, pinpointing specific areas of bias in the dataset.