NeurIPS 2018poster191 citations
Fairness Through Computationally-Bounded Awareness
Michael Kim, Omer Reingold, Guy Rothblum
Abstract
We study the problem of fair classification within the versatile framework of Dwork et al. [ITCS '12], which assumes the existence of a metric that measures similarity between pairs of individuals. Unlike earlier work, we do not assume that the entire metric is known to the learning algorithm; instead, the learner can query this
BibTeX
@inproceedings{NEURIPS2018_c8dfece5,
author = {Kim, Michael and Reingold, Omer and Rothblum, Guy},
booktitle = {Advances in Neural Information Processing Systems},
editor = {S. Bengio and H. Wallach and H. Larochelle and K. Grauman and N. Cesa-Bianchi and R. Garnett},
pages = {},
publisher = {Curran Associates, Inc.},
title = {Fairness Through Computationally-Bounded Awareness},
url = {https://proceedings.neurips.cc/paper_files/paper/2018/file/c8dfece5cc68249206e4690fc4737a8d-Paper.pdf},
volume = {31},
year = {2018}
}