Fairness-aware classification models have gained increasing attention in recent years as concerns grow on discrimination against some demographic groups. Most existing models require full knowledge of the sensitive features, which can be impractical due to privacy, legal issues, and an individual's fear of discrimination. The key challenge we will address is the group dependency of the unavailability, e.g., people of some age range may be more reluctant to reveal their age. Our solution augments general fairness risks with probabilistic imputations of the sensitive features, while jointly learning the group-conditionally missing probabilities in a variational auto-encoder. Our model is demonstrated effective on both image and tabular datasets, achieving an improved balance between accuracy and fairness.

翻译：近年来，随着对某些人口群体歧视问题的日益关注，公平感知分类模型获得了越来越多的重视。现有模型大多需要完全掌握敏感特征信息，但由于隐私、法律问题以及个体对歧视的担忧，这一要求往往难以实现。我们将解决的核心挑战是数据缺失的群体依赖性，例如某些年龄段的人群可能更不愿意透露年龄信息。我们的解决方案通过敏感特征的概率插补来增强一般公平性风险度量，同时利用变分自编码器联合学习群体条件缺失概率。实验表明，该模型在图像和表格数据集上均表现有效，在准确性与公平性之间实现了更好的平衡。