论文标题
教育算法公平性
Algorithmic Fairness in Education
论文作者
论文摘要
数据驱动的预测模型越来越多地用于教育中，以支持学生，讲师和管理员。但是，人们担心这些算法系统的预测和用途的公平性。在这项教育算法公平的简介中，我们与有关教育访问，偏见和歧视的先前文献相似，并研究了算法系统（测量，模型学习和行动）的核心组成部分，以识别开发和部署这些系统的偏见和歧视来源。对公平性的统计，基于相似性和因果关系概念被审查并以教育环境中的方式进行对比。对政策制定者和教育技术开发人员的建议为如何促进教育算法公平性提供指导。
Data-driven predictive models are increasingly used in education to support students, instructors, and administrators. However, there are concerns about the fairness of the predictions and uses of these algorithmic systems. In this introduction to algorithmic fairness in education, we draw parallels to prior literature on educational access, bias, and discrimination, and we examine core components of algorithmic systems (measurement, model learning, and action) to identify sources of bias and discrimination in the process of developing and deploying these systems. Statistical, similarity-based, and causal notions of fairness are reviewed and contrasted in the way they apply in educational contexts. Recommendations for policy makers and developers of educational technology offer guidance for how to promote algorithmic fairness in education.
