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.
翻译:教育成果的公平性和大赦国际在种族方面的公平性一直是教育中越来越重要的课题。在这项工作中,我们既要对高等教育的年级预测进行经验性评估,也是改进课程设计、规划学术支持的干预措施和向学生提供课程指导的重要任务。我们以公平为目的,在标签和实例上试验若干战略,以平衡兼顾,尽量缩小在种族方面的算法表现差异。我们发现,对抗性学习方法,加上等级标签的平衡,远在最公平的结果中得以实现。以教育成果的公平为目的,我们试验提高历来得不到充分服务的群体的预测性能的战略,并找出与这些群体历史成果成反比的抽样成功率。由于使用人工智能技术支持校园日益盛行,我们的方法填补了考虑在敏感学生属性方面业绩权衡框架的需要,并允许机构以注重公平和公平的方式使用其自学资源。