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Detecting Racial Bias in Jury Selection

Author

Listed:
  • Jack Dunn

    (Interpretable AI)

  • Ying Daisy Zhuo

    (Interpretable AI)

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.

Suggested Citation

  • Jack Dunn & Ying Daisy Zhuo, 2022. "Detecting Racial Bias in Jury Selection," SN Operations Research Forum, Springer, vol. 3(3), pages 1-17, September.
    • Handle: RePEc:spr:snopef:v:3:y:2022:i:3:d:10.1007_s43069-022-00151-x
    DOI: 10.1007/s43069-022-00151-x
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    References listed on IDEAS

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    1. David Arnold & Will Dobbie & Crystal S Yang, 2018. "Racial Bias in Bail Decisions," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 133(4), pages 1885-1932.
    2. Bertsimas, Dimitris & Copenhaver, Martin S., 2018. "Characterization of the equivalence of robustification and regularization in linear and matrix regression," European Journal of Operational Research, Elsevier, vol. 270(3), pages 931-942.
    3. Alberto Alesina & Eliana La Ferrara, 2014. "A Test of Racial Bias in Capital Sentencing," American Economic Review, American Economic Association, vol. 104(11), pages 3397-3433, November.
    4. Shamena Anwar & Patrick Bayer & Randi Hjalmarsson, 2012. "The Impact of Jury Race in Criminal Trials," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 127(2), pages 1017-1055.
    5. Hui Zou & Trevor Hastie, 2005. "Addendum: Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(5), pages 768-768, November.
    6. Hui Zou & Trevor Hastie, 2005. "Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(2), pages 301-320, April.
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