  Fairness has become a central issue for our research community as
classification algorithms are adopted in societally critical domains such as
recidivism prediction and loan approval. In this work, we consider the
potential bias based on protected attributes (e.g., race and gender), and
tackle this problem by learning latent representations of individuals that are
statistically indistinguishable between protected groups while sufficiently
preserving other information for classification. To do that, we develop a
minimax adversarial framework with a generator to capture the data distribution
and generate latent representations, and a critic to ensure that the
distributions across different protected groups are similar. Our framework
provides a theoretical guarantee with respect to statistical parity and
individual fairness. Empirical results on four real-world datasets also show
that the learned representation can effectively be used for classification
tasks such as credit risk prediction while obstructing information related to
protected groups, especially when removing protected attributes is not
sufficient for fair classification.
