  Equity of educational outcome and fairness of AI with respect to race have
been topics of increasing importance in education. In this work, we address
both with empirical evaluations of grade prediction in higher education, an
important task to improve curriculum design, plan interventions for academic
support, and offer course guidance to students. With fairness as the aim, we
trial several strategies for both label and instance balancing to attempt to
minimize differences in algorithm performance with respect to race. We find
that an adversarial learning approach, combined with grade label balancing,
achieved by far the fairest results. With equity of educational outcome as the
aim, we trial strategies for boosting predictive performance on historically
underserved groups and find success in sampling those groups in inverse
proportion to their historic outcomes. With AI-infused technology supports
increasingly prevalent on campuses, our methodologies fill a need for
frameworks to consider performance trade-offs with respect to sensitive student
attributes and allow institutions to instrument their AI resources in ways that
are attentive to equity and fairness.
