Detecting Racial Bias in Jury Selection

Jack Dunn () and Ying Daisy Zhuo ()
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Jack Dunn: Interpretable AI
Ying Daisy Zhuo: Interpretable AI

SN Operations Research Forum, 2022, vol. 3, issue 3, 1-17

Abstract: 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.

Keywords: Interpretable machine learning; Bias detection; Decision trees; Sparse regression (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s43069-022-00151-x

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