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Bias in Law School Admissions: Analysis and Mitigation Using Machine Learning
Federico Marchi • Christina Baytcheva • Anna Di Iulio
abstract
The legal profession remains one of the least diverse in the U.S., and the bar exam may play a role in that. Exploiting a Law School Admissions dataset from the Law School Admissions Council (LSAC) [1], we aim to explore how racial and ethnic disparities manifest in law school bar passage rates. Using publicly available data, we plan to examine whether schools with higher percentages of students from underrepresented racial and ethnic backgrounds tend to have lower first-time bar exam pass rates, even when controlling for academic and institutional factors. Our goal is to analyze potential sources of bias, test machine learning models for fairness, and explore strategies to reduce algorithmic and systemic inequities.
