ICML 2019oral430 citations
Flexibly Fair Representation Learning by Disentanglement
Elliot Creager, David Madras, Joern-Henrik Jacobsen, Marissa Weis, Kevin Swersky, Toniann Pitassi, Richard Zemel
Abstract
We consider the problem of learning representations that achieve group and subgroup fairness with respect to multiple sensitive attributes. Taking inspiration from the disentangled representation learning literature, we propose an algorithm for learning compact representations of datasets that are useful for reconstruction and prediction, but are also
BibTeX
@InProceedings{pmlr-v97-creager19a,
title = {Flexibly Fair Representation Learning by Disentanglement},
author = {Creager, Elliot and Madras, David and Jacobsen, Joern-Henrik and Weis, Marissa and Swersky, Kevin and Pitassi, Toniann and Zemel, Richard},
booktitle = {Proceedings of the 36th International Conference on Machine Learning},
pages = {1436--1445},
year = {2019},
editor = {Chaudhuri, Kamalika and Salakhutdinov, Ruslan},
volume = {97},
series = {Proceedings of Machine Learning Research},
month = {09--15 Jun},
publisher = {PMLR},
pdf = {http://proceedings.mlr.press/v97/creager19a/creager19a.pdf},
url = {https://proceedings.mlr.press/v97/creager19a.html},
abstract = {We consider the problem of learning representations that achieve group and subgroup fairness with respect to multiple sensitive attributes. Taking inspiration from the disentangled representation learning literature, we propose an algorithm for learning compact representations of datasets that are useful for reconstruction and prediction, but are also