Algorithmic Fairness and Structural Injustice: Insights from Feminist Political Philosophy

TL;DR

This paper critiques current algorithmic fairness approaches for neglecting structural injustices and integrates feminist political philosophy insights, especially Iris Marion Young's work, to develop a more responsible fairness paradigm.

Contribution

It introduces feminist political philosophy perspectives into algorithmic fairness, emphasizing structural injustices and proposing a responsible fairness framework.

Findings

Current fairness metrics overlook structural injustices.

Feminist philosophy highlights the importance of addressing systemic social harms.

A new paradigm of responsible algorithmic fairness is proposed.

Abstract

Data-driven predictive algorithms are widely used to automate and guide high-stake decision making such as bail and parole recommendation, medical resource distribution, and mortgage allocation. Nevertheless, harmful outcomes biased against vulnerable groups have been reported. The growing research field known as 'algorithmic fairness' aims to mitigate these harmful biases. Its primary methodology consists in proposing mathematical metrics to address the social harms resulting from an algorithm's biased outputs. The metrics are typically motivated by -- or substantively rooted in -- ideals of distributive justice, as formulated by political and legal philosophers. The perspectives of feminist political philosophers on social justice, by contrast, have been largely neglected. Some feminist philosophers have criticized the paradigm of distributive justice and have proposed corrective amendments to surmount its limitations. The present paper brings some key insights of feminist political philosophy to algorithmic fairness. The paper has three goals. First, I show that algorithmic fairness does not accommodate structural injustices in its current scope. Second, I defend the relevance of structural injustices -- as pioneered in the contemporary philosophical literature by Iris Marion Young -- to algorithmic fairness. Third, I take some steps in developing the paradigm of 'responsible algorithmic fairness' to correct for errors in the current scope and implementation of algorithmic fairness.