Toward Substantive Intersectional Algorithmic Fairness: Desiderata for a Feminist Approach

TL;DR

This paper advocates for a substantive intersectional feminist approach to algorithmic fairness, emphasizing social context, structural inequalities, and the need for reflective, transformative practices in AI system design and assessment.

Contribution

It extends Green's notion of substantive fairness with intersectional feminist insights and proposes ten desiderata to guide equitable AI practices addressing systemic inequalities.

Findings

Introduces ten desiderata for intersectional fairness in AI

Highlights importance of social context and structural inequalities

Encourages reflection on assumptions and transformative potential

Abstract

People's experiences of discrimination are often shaped by multiple intersecting factors, yet algorithmic fairness research rarely reflects this complexity. While intersectionality offers tools for understanding how forms of oppression interact, current approaches to intersectional algorithmic fairness tend to focus on narrowly defined demographic subgroups. These methods contribute important insights but risk oversimplifying social reality and neglecting structural inequalities. In this paper, we outline how a substantive approach to intersectional algorithmic fairness can reorient this research and practice. In particular, we propose Substantive Intersectional Algorithmic Fairness, extending Green's (2022) notion of substantive algorithmic fairness with insights from intersectional feminist theory. Aiming to provide as actionable guidance as possible, our approach is articulated as ten desiderata to guide the design, assessment, and deployment of algorithmic systems that address systemic inequities while mitigating harms to intersectionally marginalized communities. Rather than prescribing fixed operationalizations, these desiderata invite AI practitioners and experts to reflect on assumptions of neutrality, the use of protected attributes, the inclusion of multiply marginalized groups, and the transformative potential of algorithmic systems. By bridging computational and social science perspectives, the approach emphasizes that fairness cannot be separated from social context, and that in some cases, principled non-deployment may be necessary.