Evaluation of Fairness Trade-offs in Predicting Student Success
This addresses fairness concerns in educational predictive systems for at-risk students, but it is incremental as it applies existing methods to a specific domain.
The study tackled the problem of algorithmic bias in predictive models for student success, finding that the model exhibited gender and racial bias in two out of three fairness measures, and applied post-hoc adjustments to highlight trade-offs between these measures.
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.