Much research has been devoted to the problem of learning fair representations; however, they do not explicitly the relationship between latent representations. In many real-world applications, there may be causal relationships between latent representations. Furthermore, most fair representation learning methods focus on group-level fairness and are based on correlations, ignoring the causal relationships underlying the data. In this work, we theoretically demonstrate that using the structured representations enable downstream predictive models to achieve counterfactual fairness, and then we propose the Counterfactual Fairness Variational AutoEncoder (CF-VAE) to obtain structured representations with respect to domain knowledge. The experimental results show that the proposed method achieves better fairness and accuracy performance than the benchmark fairness methods.

翻译：大量研究致力于学习公平表示的问题，然而，这些研究并未明确考虑潜在表示之间的关系。在许多实际应用中，潜在表示之间可能存在因果关系。此外，大多数公平表示学习方法关注群体层面的公平性，并基于相关性，忽略了数据背后的因果关系。在这项工作中，我们从理论上证明，使用结构化表示能够使下游预测模型实现反事实公平，随后提出反事实公平变分自编码器（CF-VAE），以根据领域知识获取结构化表示。实验结果表明，所提方法在公平性和准确性方面均优于基准公平性方法。