Learning Adversarially Fair and Transferable Representations
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.
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David Madras (add twitter)
Elliot Creager (add twitter)
Toniann Pitassi (add twitter)
Richard Zemel (add twitter)
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07/10/18 06:33PM
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david_madras: Finally, our code is up for our ICML paper “Learning Adversarially Fair and Transferrable Representations” (https://t.co/JcyP1QeOxS)! Big thanks to my co-author Elliot Creager for his hard work on this. You can check out the paper here btw: https://t.co/EAHcWW8Sfv 3/4
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