  The need to address representation biases and sentencing disparities in legal
case data has long been recognized. Here, we study the problem of identifying
and measuring biases in large-scale legal case data from an algorithmic
fairness perspective. Our approach utilizes two regression models: A baseline
that represents the decisions of a "typical" judge as given by the data and a
"fair" judge that applies one of three fairness concepts. Comparing the
decisions of the "typical" judge and the "fair" judge allows for quantifying
biases across demographic groups, as we demonstrate in four case studies on
criminal data from Cook County (Illinois).
