Towards Counterfactual Fairness-aware Domain Generalization in Changing Environments

arXiv:2309.13005v2 Announce Type: replace
Abstract: Recognizing the prevalence of domain shift as a common challenge in machine learning, various domain generalization (DG) techniques have been developed to enhance the performance of machine learning systems when dealing with out-of-distribution (OOD) data. Furthermore, in real-world scenarios, data distributions can gradually change across a sequence of sequential domains. While current methodologies primarily focus on improving model effectiveness within these new domains, they often overlook fairness issues throughout the learning process. In response, we …

abstract arxiv challenge change counterfactual cs.ai cs.cy cs.lg data distribution domain environments fairness learning systems machine machine learning performance shift systems type world