Algorithmic Fairness in Education

TL;DR

This paper reviews the challenges and methods of ensuring fairness in algorithmic systems used in education, highlighting sources of bias and offering policy and development recommendations.

Contribution

It provides a comprehensive overview of fairness notions and identifies bias sources in educational algorithms, with guidance for promoting fairness.

Findings

Different fairness notions are contrasted in educational contexts

Sources of bias in measurement, modeling, and deployment are identified

Recommendations for policy and technology development are provided

Abstract

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.