为机器学习引入社会公正视角，构建多身份交叉分析框架
A Preliminary Framework for Intersectionality in ML Pipelines
- 基于三位奠基人理论构建交叉性应用框架
- 识别并报告现有ML研究中交叉性应用的偏差
- 帮助开发者避免工具化交叉性，促进技术公平
机器学习已成为改善人与技术及世界互动的重要手段。然而，多项研究表明，机器学习技术可能无法充分支持社会身份与经验。交叉性是一种社会学框架，强调复杂社会身份的考量，聚焦社会正义与权力关系。尽管该框架可助力开发包容所有群体的技术，但其在实际应用中常被误用或偏离本源，削弱了影响力。为此，本文回溯交叉性理论三大基石（克伦肖、科姆比黑、柯林斯，即“三C”），提出一个具有社会相关性的初步框架，用于指导机器学习解决方案中的交叉性应用。该框架可用于评估和揭示现有机器学习文献中交叉性实践的错位现象，推动更符合其初衷的使用方式。
原文摘要 · Abstract (English)
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.
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