  Though machine learning models are achieving great success, ex-tensive
studies have exposed their disadvantage of inheriting latent discrimination and
societal bias from the training data, which hinders their adoption on
high-state applications. Thus, many efforts have been taken for developing fair
machine learning models. Most of them require that sensitive attributes are
available during training to learn fair models. However, in many real-world
applications, it is usually infeasible to obtain the sensitive attribute due to
privacy or legal issues, which challenges existing fair classifiers. Though the
sensitive attribute of each data sample is unknown, we observe that there are
usually some non-sensitive features in the training data that are highly
correlated with sensitive attributes, which can be used to alleviate the bias.
Therefore, in this paper, we study a novel problem of exploring features that
are highly correlated with sensitive attributes for learning fair and accurate
classifier without sensitive attributes. We theoretically show that by
minimizing the correlation between these related features and model prediction,
we can learn a fair classifier. Based on this motivation, we propose a novel
framework which simultaneously uses these related features for accurate
prediction and regularizing the model to be fair. In addition, the model can
dynamically adjust the importance weight of each related feature to balance the
contribution of the feature on model classification and fairness. Experimental
results on real-world datasets demonstrate the effectiveness of the proposed
model for learning fair models with high classification accuracy.
