  In this paper, we advocate for representation learning as the key to
mitigating unfair prediction outcomes downstream. Motivated by a scenario where
learned representations are used by third parties with unknown objectives, we
propose and explore adversarial representation learning as a natural method of
ensuring those parties act fairly. We connect group fairness (demographic
parity, equalized odds, and equal opportunity) to different adversarial
objectives. Through worst-case theoretical guarantees and experimental
validation, we show that the choice of this objective is crucial to fair
prediction. Furthermore, we present the first in-depth experimental
demonstration of fair transfer learning and demonstrate empirically that our
learned representations admit fair predictions on new tasks while maintaining
utility, an essential goal of fair representation learning.
