  Fair representation learning aims to encode invariant representation with
respect to the protected attribute, such as gender or age. In this paper, we
design Fairness-aware Disentangling Variational AutoEncoder (FD-VAE) for fair
representation learning. This network disentangles latent space into three
subspaces with a decorrelation loss that encourages each subspace to contain
independent information: 1) target attribute information, 2) protected
attribute information, 3) mutual attribute information. After the
representation learning, this disentangled representation is leveraged for
fairer downstream classification by excluding the subspace with the protected
attribute information. We demonstrate the effectiveness of our model through
extensive experiments on CelebA and UTK Face datasets. Our method outperforms
the previous state-of-the-art method by large margins in terms of equal
opportunity and equalized odds.
