Pursuing Counterfactual Fairness via Sequential Autoencoder Across
  Domains
Yujie Lin Chen Zhao Minglai Shao Baoluo Meng Xujiang Zhao Haifeng Chen
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 introduce an innovative framework called
Counterfactual Fairness-Aware Domain Generalization with Sequential Autoencoder
(CDSAE). This approach effectively separates environmental information and
sensitive attributes from the embedded representation of classification
features. This concurrent separation not only greatly improves model
generalization across diverse and unfamiliar domains but also effectively
addresses challenges related to unfair classification. Our strategy is rooted
in the principles of causal inference to tackle these dual issues. To examine
the intricate relationship between semantic information, sensitive attributes,
and environmental cues, we systematically categorize exogenous uncertainty
factors into four latent variables: 1) semantic information influenced by
sensitive attributes, 2) semantic information unaffected by sensitive
attributes, 3) environmental cues influenced by sensitive attributes, and 4)
environmental cues unaffected by sensitive attributes. By incorporating
fairness regularization, we exclusively employ semantic information for
classification purposes. Empirical validation on synthetic and real-world
datasets substantiates the effectiveness of our approach, demonstrating
improved accuracy levels while ensuring the preservation of fairness in the
evolving landscape of continuous domains.