  Most existing fair classifiers rely on sensitive attributes to achieve
fairness. However, for many scenarios, we cannot obtain sensitive attributes
due to privacy and legal issues. The lack of sensitive attributes challenges
many existing works. Though we lack sensitive attributes, for many
applications, there usually exists features or information of various formats
that are relevant to sensitive attributes. For example, a personal purchase
history can reflect his or her race, which would be helpful for learning fair
classifiers on race. However, the work on exploring relevant features for
learning fair models without sensitive attributes is rather limited. Therefore,
in this paper, we study a novel problem of learning fair models without
sensitive attributes by exploring relevant features. We propose a probabilistic
generative framework to effectively estimate the sensitive attribute from the
training data with relevant features in various formats and utilize the
estimated sensitive attribute information to learn fair models. Experimental
results on real-world datasets show the effectiveness of our framework in terms
of both accuracy and fairness.
