Title: Identities are not Interchangeable: The Problem of Overgeneralization in Fair Machine Learning Show Chinese title

Title: 身份不可互换：公平机器学习中的过度泛化问题

Abstract: A key value proposition of machine learning is generalizability: the same methods and model architecture should be able to work across different domains and different contexts. While powerful, this generalization can sometimes go too far, and miss the importance of the specifics. In this work, we look at how fair machine learning has often treated as interchangeable the identity axis along which discrimination occurs. In other words, racism is measured and mitigated the same way as sexism, as ableism, as ageism. Disciplines outside of computer science have pointed out both the similarities and differences between these different forms of oppression, and in this work we draw out the implications for fair machine learning. While certainly not all aspects of fair machine learning need to be tailored to the specific form of oppression, there is a pressing need for greater attention to such specificity than is currently evident. Ultimately, context specificity can deepen our understanding of how to build more fair systems, widen our scope to include currently overlooked harms, and, almost paradoxically, also help to narrow our scope and counter the fear of an infinite number of group-specific methods of analysis. Show Chinese abstract

Abstract: 机器学习的一个关键价值主张是泛化能力：相同的方法和模型架构应该能够在不同领域和不同情境中发挥作用。 虽然强大，但这种泛化有时可能过于广泛，忽视了具体细节的重要性。 在本工作中，我们探讨了公平机器学习常常将歧视发生的身份轴视为可互换的问题。 换句话说，种族主义被测量和缓解的方式与性别歧视、身体障碍歧视、年龄歧视等方式相同。 计算机科学以外的学科指出了这些不同形式压迫之间的相似性和差异性，在本工作中，我们阐述了这对公平机器学习的影响。 尽管公平机器学习的各个方面并不都需要针对特定形式的压迫进行定制，但目前显然需要更多关注这种具体性。 最终，情境的具体性可以加深我们对如何构建更公平系统的理解，拓宽我们的视野以包括目前被忽视的伤害，而且几乎矛盾的是，也有助于缩小我们的视野并应对对无限数量的群体特定分析方法的恐惧。