Fairness-Aware Domain Generalization under Covariate and Dependence
  Shifts
Chen Zhao Kai Jiang Xintao Wu Haoliang Wang Latifur Khan Christan Grant Feng Chen
Achieving the generalization of an invariant classifier from source domains
to shifted target domains while simultaneously considering model fairness is a
substantial and complex challenge in machine learning. Existing domain
generalization research typically attributes domain shifts to concept shift,
which relates to alterations in class labels, and covariate shift, which
pertains to variations in data styles. In this paper, by introducing another
form of distribution shift, known as dependence shift, which involves
variations in fair dependence patterns across domains, we propose a novel
domain generalization approach that addresses domain shifts by considering both
covariate and dependence shifts. We assert the existence of an underlying
transformation model can transform data from one domain to another. By
generating data in synthetic domains through the model, a fairness-aware
invariant classifier is learned that enforces both model accuracy and fairness
in unseen domains. Extensive empirical studies on four benchmark datasets
demonstrate that our approach surpasses state-of-the-art methods.