In order to monitor and prevent bias in AI systems we can use a wide range of
(statistical) fairness measures. However, it is mathematically impossible to
optimize for all of these measures at the same time. In addition, optimizing a
fairness measure often greatly reduces the accuracy of the system (Kozodoi et
al, 2022). As a result, we need a substantive theory that informs us how to
make these decisions and for what reasons. I show that by using Rawls' notion
of justice as fairness, we can create a basis for navigating fairness measures
and the accuracy trade-off. In particular, this leads to a principled choice
focusing on both the most vulnerable groups and the type of fairness measure
that has the biggest impact on that group. This also helps to close part of the
gap between philosophical accounts of distributive justice and the fairness
literature that has been observed (Kuppler et al, 2021) and to operationalise
the value of fairness.

通过使用 Rawls 公正作为选择公平措施及取得平衡的基础，我们可以为 AI 系统中的公平 / 准确度取得权衡提供一个有原则的选择，从而集中关注最脆弱的群体和最有影响该群体的公平措施。

公平度量与权衡导航

Navigating Fairness Measures and Trade-Offs

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?

本篇论文研究如何建立更公正的机器学习系统，探讨了分配正义和能力正义两种不同的正义观念，并分析它们在公正机器学习领域的应用及局限性。同时，研究者提出了对于公正机器学习，应该如何确立合适的正义观念。

机器学习中的公正性测量

Measuring justice in machine learning

Machine learning algorithms for prediction are increasingly being used in
critical decisions affecting human lives. Various fairness formalizations, with
no firm consensus yet, are employed to prevent such algorithms from
systematically discriminating against people based on certain attributes
protected by law. The aim of this article is to survey how fairness is
formalized in the machine learning literature for the task of prediction and
present these formalizations with their corresponding notions of distributive
justice from the social sciences literature. We provide theoretical as well as
empirical critiques of these notions from the social sciences literature and
explain how these critiques limit the suitability of the corresponding fairness
formalizations to certain domains. We also suggest two notions of distributive
justice which address some of these critiques and discuss avenues for
prospective fairness formalizations.

探讨机器学习算法公平性如何在预测任务中被正式化，并提供相应的社会科学文献中分配公正的概念，通过理论和实证批判这些概念，并解释这些批判如何限制相应公平正式化的适用性和讨论潜在公平正式化的路径。