A Preliminary Framework for Intersectionality in ML Pipelines
This work addresses the need for more equitable technological outcomes by ensuring intersectionality is applied appropriately in ML, though it appears incremental as it builds on existing sociological foundations.
The paper tackles the problem of inadequate support for societal identities in machine learning by proposing a preliminary framework based on intersectionality to evaluate and report misalignments in its application within ML literature.
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.