Algorithmic fairness in education

Abstract

Data-driven predictive models are increasingly used in education to support students, instructors, and administrators, which has raised concerns about the fairness of their 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 how they apply in educational contexts. Recommendations for policymakers and developers of educational technology offer guidance for promoting algorithmic fairness in education.