  Colleges and universities are increasingly turning to algorithms that predict
college-student success to inform various decisions, including those related to
admissions, budgeting, and student-success interventions. Because predictive
algorithms rely on historical data, they capture societal injustices, including
racism. In this study, we examine how the accuracy of college student success
predictions differs between racialized groups, signaling algorithmic bias. We
also evaluate the utility of leading bias-mitigating techniques in addressing
this bias. Using nationally representative data from the Education Longitudinal
Study of 2002 and various machine learning modeling approaches, we demonstrate
how models incorporating commonly used features to predict college-student
success are less accurate when predicting success for racially minoritized
students. Common approaches to mitigating algorithmic bias are generally
ineffective at eliminating disparities in prediction outcomes and accuracy
between racialized groups.
