A Preliminary Framework for Intersectionality in ML Pipelines

TL;DR

This paper introduces a preliminary framework based on foundational intersectionality scholarship to evaluate and improve the application of intersectionality in machine learning, aiming for more equitable outcomes.

Contribution

It develops a novel framework grounded in core intersectionality theories to assess how ML research incorporates social identities and justice considerations.

Findings

Identifies gaps in current ML practices regarding intersectionality

Provides a tool for evaluating intersectionality alignment in ML research

Highlights the need for more socially relevant ML development

Abstract

Machine learning (ML) has become a go-to solution for improving how we use, experience, and interact with technology (and the world around us). Unfortunately, studies have repeatedly shown that machine learning technologies may not provide adequate support for societal identities and experiences. Intersectionality is a sociological framework that provides a mechanism for explicitly considering complex social identities, focusing on social justice and power. While the framework of intersectionality can support the development of technologies that acknowledge and support all members of society, it has been adopted and adapted in ways that are not always true to its foundations, thereby weakening its potential for impact. To support the appropriate adoption and use of intersectionality for more equitable technological outcomes, we amplify the foundational intersectionality scholarship--Crenshaw, Combahee, and Collins (three C's), to create a socially relevant preliminary framework in developing machine-learning solutions. We use this framework to evaluate and report on the (mis)alignments of intersectionality application in machine learning literature.