Fairness without Harm: Decoupled Classifiers with Preference Guarantees

Berk Ustun; Yang Liu; David Parkes

ICML 2019oral163 citations

Fairness without Harm: Decoupled Classifiers with Preference Guarantees

Berk Ustun, Yang Liu, David Parkes

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Abstract

In domains such as medicine, it can be acceptable for machine learning models to include

BibTeX

@InProceedings{pmlr-v97-ustun19a,
  title = 	 {Fairness without Harm: Decoupled Classifiers with Preference Guarantees},
  author =       {Ustun, Berk and Liu, Yang and Parkes, David},
  booktitle = 	 {Proceedings of the 36th International Conference on Machine Learning},
  pages = 	 {6373--6382},
  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/ustun19a/ustun19a.pdf},
  url = 	 {https://proceedings.mlr.press/v97/ustun19a.html},
  abstract = 	 {In domains such as medicine, it can be acceptable for machine learning models to include