  As machine learning (ML) algorithms are increasingly used in high-stakes
applications, concerns have arisen that they may be biased against certain
social groups. Although many approaches have been proposed to make ML models
fair, they typically rely on the assumption that data distributions in training
and deployment are identical. Unfortunately, this is commonly violated in
practice and a model that is fair during training may lead to an unexpected
outcome during its deployment. Although the problem of designing robust ML
models under dataset shifts has been widely studied, most existing works focus
only on the transfer of accuracy. In this paper, we study the transfer of both
fairness and accuracy under domain generalization where the data at test time
may be sampled from never-before-seen domains. We first develop theoretical
bounds on the unfairness and expected loss at deployment, and then derive
sufficient conditions under which fairness and accuracy can be perfectly
transferred via invariant representation learning. Guided by this, we design a
learning algorithm such that fair ML models learned with training data still
have high fairness and accuracy when deployment environments change.
Experiments on real-world data validate the proposed algorithm. Model
implementation is available at https://github.com/pth1993/FATDM.
