论文标题
评估在预测学生成功方面的公平权衡取舍
Evaluation of Fairness Trade-offs in Predicting Student Success
论文作者
论文摘要
早期识别高危学生的预测模型可以帮助教职员工指导资源更好地支持他们，但是人们对算法在教育中的公平性越来越关注。预测模型可能会无意中引入偏见，谁会获得支持，从而加剧现有的不平等现象。我们通过根据大学行政记录建立学生成功的预测模型来研究这个问题。我们发现，该模型在考虑的三项公平措施中的两个中表现出性别和种族偏见。然后，我们采用事后调整以提高模型公平性，以突出三种公平措施之间的权衡。
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.
