The politics and reciprocal (re)configuration of accountability and fairness in data-driven education

Abstract

As awareness of bias in educational machine learning applications increases, accountability for technologies and their impact on educational equality is becoming an increasingly important constituent of ethical conduct and accountability in education. This article critically examines the relationship between so-called algorithmic fairness and algorithmic accountability in education. I argue that operative political meanings of accountability and fairness are constructed, operationalized, and reciprocally configured in the performance of algorithmic accountability in education. Tools for measuring forms of unwanted bias in machine learning systems, and technical fixes for mitigating them, are value-laden and may conceal the politics behind quantifying educational inequality. Crucially, some approaches may also disincentivize systemic reforms for substantive equality in education in the name of accountability.