Abstract
Fairness evaluation in face analysis systems (FAS) typically depends on automatic demographic attribute inference (DAI), which itself relies on predefined demographic segmentation. However, the validity of fairness auditing hinges on the reliability of the DAI process. We begin by providing a theoretical motivation for this dependency, showing that improved DAI reliability leads to less biased and lower-variance estimates of FAS fairness. To address this, we propose a fully reproducible DAI pipeline that replaces conventional end-to-end training with a modular transfer learning approach. Our design integrates pretrained face recognition encoders with non-linear classification heads. We audit this pipeline across three dimensions: accuracy, fairness, and a newly introduced notion of robustness, defined via intra-identity consistency. The proposed robustness metric is applicable to any demographic segmentation scheme. We benchmark the pipeline on gender and ethnicity inference across multiple datasets and training setups. Our results show that the proposed method outperforms strong baselines, particularly on ethnicity, which is the more challenging attribute. To promote transparency and reproducibility, we will publicly release the training dataset metadata, full codebase, pretrained models, and evaluation toolkit. This work contributes a reliable foundation for demographic inference in fairness auditing.
Abstract (translated)
面部分析系统（FAS）中的公平性评估通常依赖于自动人口属性推断（DAI），而后者又基于预定义的人口细分。然而，公平审计的有效性取决于DAI过程的可靠性。我们首先提供这种依赖关系的理论动机，并表明改进了DAI的可靠性可以减少对FAS公平性的偏差和方差估计。为了解决这一问题，我们提出了一种完全可重现的DAI流水线，该流水线用模块化的迁移学习方法代替传统的端到端训练。我们的设计将预训练的人脸识别编码器与非线性分类头部相结合。我们在准确率、公平性和一种新引入的鲁棒性的概念上对这一流程进行审核，后者通过同一身份内的一致性来定义。我们提出的鲁棒性度量适用于任何人口细分方案。 在多个数据集和训练设置下，我们对该流水线进行了性别和族裔推断基准测试。实验结果显示，所提出的方法优于强大的基线方法，在更难处理的族裔属性上尤其如此。为了促进透明性和可重现性，我们将公开发布用于训练的数据集元信息、完整代码库、预训练模型以及评估工具包。 这项工作为公平审计中的人口统计推断提供了一个可靠的基石。
URL
https://arxiv.org/abs/2510.20482
