Web: http://arxiv.org/abs/2209.07190

Sept. 16, 2022, 1:11 a.m. | Mengdi Zhang, Jun Sun

cs.LG updates on arXiv.org arxiv.org

Given a discriminating neural network, the problem of fairness improvement is
to systematically reduce discrimination without significantly scarifies its
performance (i.e., accuracy). Multiple categories of fairness improving methods
have been proposed for neural networks, including pre-processing, in-processing
and post-processing. Our empirical study however shows that these methods are
not always effective (e.g., they may improve fairness by paying the price of
huge accuracy drop) or even not helpful (e.g., they may even worsen both
fairness and accuracy). In this work, …

analysis arxiv causality fairness improvement

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