Most existing works on fairness assume the model has full access to demographic information. However, there exist scenarios where demographic information is partially available because a record was not maintained throughout data collection or due to privacy reasons. This setting is known as demographic scarce regime. Prior research have shown that training an attribute classifier to replace the missing sensitive attributes (proxy) can still improve fairness. However, the use of proxy-sensitive attributes worsens fairness-accuracy trade-offs compared to true sensitive attributes. To address this limitation, we propose a framework to build attribute classifiers that achieve better fairness-accuracy trade-offs. Our method introduces uncertainty awareness in the attribute classifier and enforces fairness on samples with demographic information inferred with the lowest uncertainty. We show empirically that enforcing fairness constraints on samples with uncertain sensitive attributes is detrimental to fairness and accuracy. Our experiments on two datasets showed that the proposed framework yields models with significantly better fairness-accuracy trade-offs compared to classic attribute classifiers. Surprisingly, our framework outperforms models trained with constraints on the true sensitive attributes.

翻译：现有大多数公平性研究假设模型可完全获取人口统计信息。然而，现实中存在因数据采集过程中记录缺失或隐私保护等原因导致人口统计信息部分可得的情况，这种情景被称为人口稀少环境。已有研究表明，训练属性分类器替代缺失的敏感属性（代理）仍可改善公平性。但相较于真实敏感属性，使用代理敏感属性会加剧公平性-准确性权衡的困境。为解决这一局限，我们提出一个构建属性分类器的框架，以实现更优的公平性-准确性权衡。该方法在属性分类器中引入不确定性感知机制，并对以最低不确定性推断出人口统计信息的样本施加公平性约束。实证表明，对敏感属性推断结果不确定的样本施加公平性约束将损害公平性与准确性。在两个数据集上的实验显示，相较于经典属性分类器，本框架构建的模型在公平性-准确性权衡方面表现显著更优。令人意外的是，本框架甚至优于直接使用真实敏感属性约束训练的模型。