FEED: Fairness-Enhanced Meta-Learning for Domain Generalization

Generalizing to out-of-distribution data while ensuring model fairness presents a significant challenge in meta-learning. This problem aims to find fairness-aware, invariant classifier parameters trained on data from related training domains that experience distribution shifts in non-sensitive features and varying dependencies between model predictions and sensitive attributes. Such parameters enable classifiers to generalize effectively to unknown, distinct test domains. Existing state-of-the-art methods either focus on domain generalization without considering fairness or exclusively model domain shifts at varying fairness levels. In this paper, we introduce Fairness-Enhanced Meta-Learning for Domain Generalization (FEED), a novel framework that disentangles latent data representations into content, style, and sensitive vectors. This disentanglement approach promotes robust model generalization across diverse domains while rigorously upholding fairness constraints. Unlike conventional methods that concentrate on domain invariance or sensitivity to distributional shifts, our model embeds a fairnessaware invariance criterion within the meta-learning process, ensuring consistently fair learned parameters across domains with varied characteristics. Extensive experiments on multiple benchmarks validate our framework’s effectiveness, demonstrating not only superior accuracy and fairness maintenance but also substantial improvements over state-of-the-art methods in domain generalization tasks.