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.

本文旨在探讨用表示学习降低预测结果中的不公平因素。针对第三方使用这些学习到的表示却又不明确目标的情境，我们提出了对抗性表示学习方法来确保使用方的公平性。我们将群体公平（人口平等、平等赔率和平等机会）与不同的对抗目标联系起来，通过最坏情况的理论保证和实验验证，我们证明了对该目标的选择对于公平预测是至关重要的。此外，我们还展示了公平迁移学习的首个深入实验演示，并经验证明了我们学习到的表示在保持实用性的同时对新任务具有公平预测的能力，这是公平表示学习的重要目标。