In 1986, the U.S. Supreme Court ruled that using race as a reason to remove potential
jurors is unconstitutional.
Despite this ruling, there remains a large disparity in juror strike rates by race.
In 2019, the Flowers vs. Mississippi Supreme Court Case determined the Fifth Circuit Court
of Mississippi district attorney discriminated juror selection based on race.
In this module, we present a method to investigate and measure existing biases in decision making
processes.
Specifically, we demonstrate how to use predictive and cohort analysis to identify biases, and
we do this in three steps.
First, we perform data exploration and define the target variable.
Three data sets from 1992-2017 include trial, juror, and juror question data.
The target variable is juror removal.
Second, we develop a model using optimal feature selection to predict juror removal and identify
key variables.
Such variables include whether the juror candidate has been accused of a crime and whether the
juror personally or professionally knows the defendant.
Third, we feed subsets of data into the predictive algorithm, for example, population cohorts
defined by race.
We then inspect the outputs of this subset analysis to identify biases.
Upon inspection, we can not only identify problematic areas, we also know where to focus
to address change.