  Modern machine learning (ML) models are becoming increasingly popular and are
widely used in decision-making systems. However, studies have shown critical
issues of ML discrimination and unfairness, which hinder their adoption on
high-stake applications. Recent research on fair classifiers has drawn
significant attention to developing effective algorithms to achieve fairness
and good classification performance. Despite the great success of these
fairness-aware machine learning models, most of the existing models require
sensitive attributes to pre-process the data, regularize the model learning or
post-process the prediction to have fair predictions. However, sensitive
attributes are often incomplete or even unavailable due to privacy, legal or
regulation restrictions. Though we lack the sensitive attribute for training a
fair model in the target domain, there might exist a similar domain that has
sensitive attributes. Thus, it is important to exploit auxiliary information
from a similar domain to help improve fair classification in the target domain.
Therefore, in this paper, we study a novel problem of exploring domain
adaptation for fair classification. We propose a new framework that can learn
to adapt the sensitive attributes from a source domain for fair classification
in the target domain. Extensive experiments on real-world datasets illustrate
the effectiveness of the proposed model for fair classification, even when no
sensitive attributes are available in the target domain.
