This paper studies learning fair encoders in a self-supervised learning (SSL)
setting, in which all data are unlabeled and only a small portion of them are
annotated with sensitive attribute.
Adversarial fair representation learning is well suited for this scenario by
minimizing a contrastive loss over unlabeled data while maximizing an
adversarial loss of predicting the sensitive attribute over the data with
sensitive attribute. Nevertheless, optimizing adversarial fair representation
learning presents significant challenges due to solving a non-convex
non-concave minimax game. The complexity deepens when incorporating a global
contrastive loss that contrasts each anchor data point against all other
examples. A central question is ``{\it can we design a provable yet efficient
algorithm for solving adversarial fair self-supervised contrastive learning}?''
Building on advanced optimization techniques, we propose a stochastic algorithm
dubbed SoFCLR with a convergence analysis under reasonable conditions without
requring a large batch size. We conduct extensive experiments to demonstrate
the effectiveness of the proposed approach for downstream classification with
eight fairness notions.

研究自监督学习中公平编码器的学习，在这种情况下，所有数据都没有标签，并且只有其中一小部分数据带有敏感属性的注释。通过最小化无标签数据上的对比损失和最大化在具有敏感属性的数据上预测敏感属性的对抗损失，对抗公平表示学习非常适合这种情况。通过构建先进的优化技术，并在合理条件下提出了一种称为 SoFCLR 的随机算法，并对其收敛性进行了分析，而无需大数据批处理大小。通过广泛的实验证明了所提方法在下游分类中八个公平概念的有效性。