  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.
