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，随着对某些人群群体歧视问题的关注日益增加，公平感知分类模型备受关注。然而，现有大多数模型需要完全了解敏感特征，这由于隐私、法律问题以及个体对歧视的恐惧而往往不切实际。我们将解决的关键挑战是信息不可用性的群体依赖性，例如，某些年龄段的人可能更不愿意透露其年龄。我们的解决方案通过概率插补敏感特征来增强通用公平性风险，同时在变分自编码器中联合学习群体条件性缺失概率。我们模型在图像和表格数据集上均显示出有效性，实现了准确性与公平性之间更优的平衡。