  Decision making is a process that is extremely prone to different biases. In
this paper we consider learning fair representations that aim at removing
nuisance (sensitive) information from the decision process. For this purpose,
we propose to use deep generative modeling and adapt a hierarchical Variational
Auto-Encoder to learn these fair representations. Moreover, we utilize the
mutual information as a useful regularizer for enforcing fairness of a
representation. In experiments on two benchmark datasets and two scenarios
where the sensitive variables are fully and partially observable, we show that
the proposed approach either outperforms or performs on par with the current
best model.
