Fairness-Aware Learning with Restriction of Universal Dependency using f-Divergences

25 Jun 2015Kazuto FukuchiJun Sakuma

Fairness-aware learning is a novel framework for classification tasks. Like regular empirical risk minimization (ERM), it aims to learn a classifier with a low error rate, and at the same time, for the predictions of the classifier to be independent of sensitive features, such as gender, religion, race, and ethnicity... (read more)

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