Towards a Critical Race Methodology in Algorithmic Fairness

TL;DR

This paper critiques current algorithmic fairness methods for treating race as a fixed attribute and advocates for a critical race theory approach that considers race as a social construct and structural phenomenon.

Contribution

It introduces a critical race methodology for algorithmic fairness, emphasizing race's social and structural dimensions and integrating insights from sociology and public health.

Findings

Current methods oversimplify race as an attribute.

A critical race approach highlights social processes of racial inequality.

Incorporating social perspectives improves fairness analysis.

Abstract

We examine the way race and racial categories are adopted in algorithmic fairness frameworks. Current methodologies fail to adequately account for the socially constructed nature of race, instead adopting a conceptualization of race as a fixed attribute. Treating race as an attribute, rather than a structural, institutional, and relational phenomenon, can serve to minimize the structural aspects of algorithmic unfairness. In this work, we focus on the history of racial categories and turn to critical race theory and sociological work on race and ethnicity to ground conceptualizations of race for fairness research, drawing on lessons from public health, biomedical research, and social survey research. We argue that algorithmic fairness researchers need to take into account the multidimensionality of race, take seriously the processes of conceptualizing and operationalizing race, focus on social processes which produce racial inequality, and consider perspectives of those most affected by sociotechnical systems.