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.

翻译：为支持2019年美国最高法院“Flowers诉密西西比州”一案,APM报告整理了历史法庭记录,以评估国家是否在攻击潜在陪审员时表现出种族偏见,这项分析使用了后向后一步的后勤倒退,得出种族是一个重要因素的结论,然而,这种选择相关特征的方法只是杂乱无章,而且不能考虑各特征之间的相互作用。 我们采用最佳特征选择来确定全球最佳特征,并肯定在罢工决定中有大量种族偏见的证据。 我们还利用最佳分类树将具有类似特征和被击中可能性的陪审员人口分组分开,并发现其中三个分组在罢工率方面表现出严重的种族差异,确定了数据集中具体的偏见领域。