  Predictive models for identifying at-risk students early can help teaching
staff direct resources to better support them, but there is a growing concern
about the fairness of algorithmic systems in education. Predictive models may
inadvertently introduce bias in who receives support and thereby exacerbate
existing inequities. We examine this issue by building a predictive model of
student success based on university administrative records. We find that the
model exhibits gender and racial bias in two out of three fairness measures
considered. We then apply post-hoc adjustments to improve model fairness to
highlight trade-offs between the three fairness measures.
