通过优化中心点得分提升人脸识别公平性，兼顾准确率与公正性。
Mitigating Bias in Facial Recognition Systems: Centroid Fairness Loss Optimization
- 基于中心点得分设计新损失函数，后处理优化预训练模型。
- 实验表明公平性显著提升，全局准确率保持不变。
- 适合关注算法偏见、需部署公平人脸识别的开发者。
社会对公平人工智能系统的需求日益迫切，要求预测模型不仅整体准确，还需满足公平性标准，避免在性别、种族、年龄等敏感属性上出现差异性误判。特别是某些人脸识别系统在特定人群中的误差分布不均，已引发监管机构不满。设计公平的人脸识别系统极具挑战，主要源于该领域性能度量（如ROC曲线）的复杂性，以及训练数据集的巨大异质性。本文提出一种新颖的后处理方法，通过优化基于中心点得分的回归损失，改进预训练人脸识别模型的公平性。该方法计算高效，实验结果充分验证了其在提升公平性方面的有效性，并能保持全局准确性。
原文摘要 · Abstract (English)
The urging societal demand for fair AI systems has put pressure on the research community to develop predictive models that are not only globally accurate but also meet new fairness criteria, reflecting the lack of disparate mistreatment with respect to sensitive attributes ($\textit{e.g.}$ gender, ethnicity, age). In particular, the variability of the errors made by certain Facial Recognition (FR) systems across specific segments of the population compromises the deployment of the latter, and was judged unacceptable by regulatory authorities. Designing fair FR systems is a very challenging problem, mainly due to the complex and functional nature of the performance measure used in this domain ($\textit{i.e.}$ ROC curves) and because of the huge heterogeneity of the face image datasets usually available for training. In this paper, we propose a novel post-processing approach to improve the fairness of pre-trained FR models by optimizing a regression loss which acts on centroid-based scores. Beyond the computational advantages of the method, we present numerical experiments providing strong empirical evidence of the gain in fairness and of the ability to preserve global accuracy.
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