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Motivated by a scenario where\nlearned representations are used by third parties with unknown objectives, we\npropose and explore adversarial representation learning as a natural method of\nensuring those parties act fairly. We connect group fairness (demographic\nparity, equalized odds, and equal opportunity) to different adversarial\nobjectives. Through worst-case theoretical guarantees and experimental\nvalidation, we show that the choice of this objective is crucial to fair\nprediction. 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