Fairness Without Demographics in Repeated Loss Minimization. Hashimoto, T., Srivastava, M., Namkoong, H., & Liang, P. In International Conference on Machine Learning, pages 1929–1938. Paper abstract bibtex Machine learning models (e.g., speech recognizers) trained on average loss suffer from representation disparity—minority groups (e.g., non-native speakers) carry less weight in the training objecti...
@inproceedings{hashimoto_fairness_2018,
title = {Fairness Without Demographics in Repeated Loss Minimization},
url = {http://proceedings.mlr.press/v80/hashimoto18a.html},
abstract = {Machine learning models (e.g., speech recognizers) trained on average loss suffer from representation disparity—minority groups (e.g., non-native speakers) carry less weight in the training objecti...},
eventtitle = {International Conference on Machine Learning},
pages = {1929--1938},
booktitle = {International Conference on Machine Learning},
author = {Hashimoto, Tatsunori and Srivastava, Megha and Namkoong, Hongseok and Liang, Percy},
urldate = {2019-07-12},
date = {2018-07-03},
langid = {english}
}
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