Fairness across domains: a unified fairness-aware framework for domain generalization and unsupervised adaptation

Fairness in machine learning remains a critical challenge, particularly in the presence of domain shift. We propose a unified fairness-aware framework for both domain generalization (DG) and unsupervised domain adaptation (UDA), which jointly addresses domain shift and sensitive-attribute bias through disentangled representation learning. The framework disentangles content, style, and sensitive factors, and uses them to generate augmented samples that reduce bias while maintaining predictive reliability. Extensive experiments on four datasets demonstrate that the proposed method achieves state-of-the-art performance in both DG and UDA settings. Moreover, it yields a stronger balance between classification accuracy and fairness across diverse domains and sensitive subgroups. By incorporating unlabeled target-domain data, our framework extends prior fairness-aware approaches that were limited to DG and provides new insight into fairness-aware learning under unsupervised adaptation. Overall, this work offers a practical step toward scalable and robust fairness-aware learning in multi-domain environments.