When building classification systems with demographic fairness
considerations, there are two objectives to satisfy: 1) maximizing utility for
the specific task and 2) ensuring fairness w.r.t. a known demographic
attribute. These objectives often compete, so optimizing both can lead to a
trade-off between utility and fairness. While existing works acknowledge the
trade-offs and study their limits, two questions remain unanswered: 1) What are
the optimal trade-offs between utility and fairness? and 2) How can we
numerically quantify these trade-offs from data for a desired prediction task
and demographic attribute of interest? This paper addresses these questions. We
introduce two utility-fairness trade-offs: the Data-Space and Label-Space
Trade-off. The trade-offs reveal three regions within the utility-fairness
plane, delineating what is fully and partially possible and impossible. We
propose U-FaTE, a method to numerically quantify the trade-offs for a given
prediction task and group fairness definition from data samples. Based on the
trade-offs, we introduce a new scheme for evaluating representations. An
extensive evaluation of fair representation learning methods and
representations from over 1000 pre-trained models revealed that most current
approaches are far from the estimated and achievable fairness-utility
trade-offs across multiple datasets and prediction tasks.

在构建考虑人口统计公平性的分类系统时，需要满足两个目标：一是在特定任务中最大化效用，二是确保对已知人口统计属性的公平性。然而，这两个目标往往存在竞争，因此在优化两者时可能需要权衡。本文回答了两个问题：一是效用和公平之间的最佳权衡是什么？二是如何从数据中量化这些权衡以用于所需的预测任务和人口统计属性？我们引入了两种效用 - 公平权衡：数据空间权衡和标签空间权衡，并揭示了在效用 - 公平平面上的三个区域，界定了完全可能、部分可能和不可能的情况。基于这些权衡，我们提出了一种用于根据数据样本从量化预测任务和群体公平性定义的权衡的方法 U-FaTE。对超过 1000 个预训练模型中的公平表示方法和表示进行了广泛评估，结果显示大多数当前方法在多个数据集和预测任务上与估算和可实现的公平 - 效用权衡相距甚远。

效用公平交替及其搜索方法

Utility-Fairness Trade-Offs and How to Find Them

Integration of speech into healthcare has intensified privacy concerns due to
its potential as a non-invasive biomarker containing individual biometric
information. In response, speaker anonymization aims to conceal personally
identifiable information while retaining crucial linguistic content. However,
the application of anonymization techniques to pathological speech, a critical
area where privacy is especially vital, has not been extensively examined. This
study investigates anonymization's impact on pathological speech across over
2,700 speakers from multiple German institutions, focusing on privacy,
pathological utility, and demographic fairness. We explore both training-based
and signal processing-based anonymization methods, and document substantial
privacy improvements across disorders-evidenced by equal error rate increases
up to 1933%, with minimal overall impact on utility. Specific disorders such as
Dysarthria, Dysphonia, and Cleft Lip and Palate experienced minimal utility
changes, while Dysglossia showed slight improvements. Our findings underscore
that the impact of anonymization varies substantially across different
disorders. This necessitates disorder-specific anonymization strategies to
optimally balance privacy with diagnostic utility. Additionally, our fairness
analysis revealed consistent anonymization effects across most of the
demographics. This study demonstrates the effectiveness of anonymization in
pathological speech for enhancing privacy, while also highlighting the
importance of customized approaches to account for inversion attacks.

对匿名化技术在病理性语音中的影响进行了研究，发现它可以增强隐私性，同时强调了为了应对逆向攻击，需要采用定制化方法来平衡隐私和诊断效用。