Web: http://arxiv.org/abs/1910.00411

May 13, 2022, 1:10 a.m. | Peter Kairouz, Jiachun Liao, Chong Huang, Maunil Vyas, Monica Welfert, Lalitha Sankar

stat.ML updates on arXiv.org arxiv.org

We present a data-driven framework for learning fair universal
representations (FUR) that guarantee statistical fairness for any learning task
that may not be known a priori. Our framework leverages recent advances in
adversarial learning to allow a data holder to learn representations in which a
set of sensitive attributes are decoupled from the rest of the dataset. We
formulate this as a constrained minimax game between an encoder and an
adversary where the constraint ensures a measure of usefulness (utility) …

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