  We present a data-driven framework for learning fair universal
representations (FUR) that guarantee statistical fairness for any learning task
that may not be known a priori. Our framework leverages recent advances in
adversarial learning to allow a data holder to learn representations in which a
set of sensitive attributes are decoupled from the rest of the dataset. We
formulate this as a constrained minimax game between an encoder and an
adversary where the constraint ensures a measure of usefulness (utility) of the
representation. The resulting problem is that of censoring, i.e., finding a
representation that is least informative about the sensitive attributes given a
utility constraint. For appropriately chosen adversarial loss functions, our
censoring framework precisely clarifies the optimal adversarial strategy
against strong information-theoretic adversaries; it also achieves the fairness
measure of demographic parity for the resulting constrained representations. We
evaluate the performance of our proposed framework on both synthetic and
publicly available datasets. For these datasets, we use two tradeoff measures:
censoring vs. representation fidelity and fairness vs. utility for downstream
tasks, to amply demonstrate that multiple sensitive features can be effectively
censored even as the resulting fair representations ensure accuracy for
multiple downstream tasks.
