Disentangled Representation with Causal Constraints for Counterfactual Fairness

• URL: http://arxiv.org/abs/2208.09147v2
• Date: Sat, 16 Dec 2023 01:33:12 GMT
• Title: Disentangled Representation with Causal Constraints for Counterfactual Fairness
• Authors: Ziqi Xu and Jixue Liu and Debo Cheng and Jiuyong Li and Lin Liu and Ke Wang
• Abstract summary: This work theoretically demonstrates that using the structured representations enable downstream predictive models to achieve counterfactual fairness. 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.
• Score: 25.114619307838602
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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