How can we build more just machine learning systems? To answer this question, we need to know both what justice is and how to tell whether one system is more or less just than another. That is, we need both a definition and a measure of justice. Theories of distributive justice hold that justice can be measured (in part) in terms of the fair distribution of benefits and burdens across people in society. Recently, the field known as fair machine learning has turned to John Rawls's theory of distributive justice for inspiration and operationalization. However, philosophers known as capability theorists have long argued that Rawls's theory uses the wrong measure of justice, thereby encoding biases against people with disabilities. If these theorists are right, is it possible to operationalize Rawls's theory in machine learning systems without also encoding its biases? In this paper, I draw on examples from fair machine learning to suggest that the answer to this question is no: the capability theorists' arguments against Rawls's theory carry over into machine learning systems. But capability theorists don't only argue that Rawls's theory uses the wrong measure, they also offer an alternative measure. Which measure of justice is right? And has fair machine learning been using the wrong one?

中文翻译：

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在机器学习中衡量正义

我们如何才能构建更多的机器学习系统？要回答这个问题，我们需要知道什么是正义以及如何判断一个系统是否比另一个系统更公正。也就是说，我们需要一个正义的定义和一个衡量标准。分配正义理论认为，正义可以（部分地）根据社会中人们的利益和负担的公平分配来衡量。最近，被称为公平机器学习的领域已经转向约翰罗尔斯的分配正义理论以获得灵感和操作化。然而，被称为能力理论家的哲学家长期以来一直认为，罗尔斯的理论使用了错误的正义衡量标准，从而编码了对残疾人的偏见。如果这些理论家是对的，是否有可能将罗尔斯的理论付诸实践？s 理论在机器学习系统中没有同时编码其偏差？在本文中，我借鉴了公平机器学习的例子来表明这个问题的答案是否定的：能力理论家反对罗尔斯理论的论点延续到机器学习系统中。但是能力理论家不仅认为罗尔斯的理论使用了错误的衡量标准，而且还提供了另一种衡量标准。哪种正义衡量标准是正确的？公平的机器学习是否使用了错误的方法？s 理论使用了错误的衡量标准，但他们也提供了另一种衡量标准。哪种正义衡量标准是正确的？公平的机器学习是否使用了错误的方法？s 理论使用了错误的度量，但他们也提供了替代度量。哪种正义衡量标准是正确的？公平的机器学习是否使用了错误的方法？