Identifying biases in legal data: An algorithmic fairness perspective

Authors: Jackson Sargent and Melanie Weber

Venue: ACM Conference on Equity and Access in Algorithms, Mechanisms, and Optimization


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).

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