  Adversarial learning is a widely used technique in fair representation
learning to remove the biases on sensitive attributes from data
representations. It usually requires to incorporate the sensitive attribute
labels as prediction targets. However, in many scenarios the sensitive
attribute labels of many samples can be unknown, and it is difficult to train a
strong discriminator based on the scarce data with observed attribute labels,
which may lead to generate unfair representations. In this paper, we propose a
semi-supervised fair representation learning approach based on adversarial
variational autoencoder, which can reduce the dependency of adversarial fair
models on data with labeled sensitive attributes. More specifically, we use a
bias-aware model to capture inherent bias information on sensitive attribute by
accurately predicting sensitive attributes from input data, and we use a
bias-free model to learn debiased fair representations by using adversarial
learning to remove bias information from them. The hidden representations
learned by the two models are regularized to be orthogonal. In addition, the
soft labels predicted by the two models are further integrated into a
semi-supervised variational autoencoder to reconstruct the input data, and we
apply an additional entropy regularization to encourage the attribute labels
inferred from the bias-free model to be high-entropy. In this way, the
bias-aware model can better capture attribute information while the bias-free
model is less discriminative on sensitive attributes if the input data is well
reconstructed. Extensive experiments on two datasets for different tasks
validate that our approach can achieve good representation learning fairness
under limited data with sensitive attribute labels.

Machine Learning