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Interdisciplinary

Bayesian Detection of Bias in Peremptory Challenges Using Historical Strike Data

ORCID Icon, ORCID Icon, ORCID Icon & ORCID Icon
Pages 209-219 | Received 01 Jul 2022, Accepted 17 Jul 2023, Published online: 02 Oct 2023
 

Abstract

United States law bars using peremptory strikes during jury selection because of prospective juror race, ethnicity, sex, or membership in certain other cognizable classes. Here, we extend a Bayesian approach for detecting such illegal strike bias by showing how to incorporate historical data on an attorney’s use of peremptory strikes in past cases. In so doing, we use the power prior to adjust the weight of such historical information in the analysis. Using simulations, we show how the choice of the power prior’s discounting parameter influences bias detection (how likely the credible interval for the bias parameter excludes zero), depending on the degree of incompatibility between current and historical trial data. Finally, we extend this approach with a prototype software application that lawyers could use to detect strike bias in real time during jury-selection. We illustrate this application’s use with real historical strike data from a convenience sample of cases from one court.

Supplementary Materials

Supplementary materials provide additional simulation results and details about the software application.

Acknowledgments

We thank the Associate Editor and two anonymous reviewers for their comments.

Authors’ Contributions

Pandya: Conceptualization, Investigation, Software, Writing; Li: Conceptualization, Formal Analysis, Software, Writing - original draft; Barón: Formal Analysis, Software; Moore: Conceptualization, Supervision, Data Visualization, Writing - original draft.

Colophon

Figures were produced with ggplot2 (Wickham Citation2016) and Tikz (Tantau Citation2022).

Disclosure Statement

The authors report there are no competing interests to declare.

Data Availability Statement

Code for simulations and associated Figures are available at DOI:10.5281/zenodo.8209028. The software app is available as an R package at https://github.com/sachinspandya/batson.app.pkg.

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