一句话：提出基于Crenshaw、Combahee与Collins的交叉性初步框架，用于评估ML流水线中的交叉性应用一致性。
机器学习技术常被用于改善技术体验，但研究反复表明其未能充分支持社会身份与经验。交叉性作为社会学框架，强调社会正义与权力，能帮助技术包容所有社会成员，但其在技术领域的采用常偏离原有基础，削弱影响。本文放大 Crenshaw、Combahee 与 Collins（三个 C）的交叉性基础学术，构建一个面向机器学习方案开发的初步、社会相关的框架。作者用该框架评估并报告机器学习文献中交叉性应用的（不）一致之处，以促进更公平的技术结果。
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.
@inproceedings{paperbot9164,
title = {A Preliminary Framework for Intersectionality in ML Pipelines},
author = {Michelle Nashla Turcios and Alicia E. Boyd and Angela D. R. Smith and Brittany Johnson},
booktitle = {FSE},
year = {2025},
doi = {10.1145/3696630.3728692}
}