SupplementaryMaterial

Fair machine learning.Generally, fair machine learning methods fall into three categories: preprocessing, in-processing, and post-processing [44, 7]. In this paper, we focus on in-processing methods thatmodify learning algorithms toremovediscrimination during thetraining process. All of those works are for indistribution fairness, and we investigate out-of-distribution fairness in this paper. Weuse LAFTR [42],anadversarial learning method that showsadvanced performance onfairness [47],tolearn a fair model in the source domain and adapt it to the target domain. We also test CFair[72] in our experiments.