As AI becomes more accessible, higher education is increasingly turning to prediction algorithms to inform decisions and target support services. Prediction algorithms can underestimate success for Black and Hispanic students, disproportionately predicting failure erroneously, even when those students ultimately graduate. Bias-mitigation techniques built into model training are more effective than those applied to the data beforehand, but no single method eliminates disparities.
