Research

Selective Regression under Fairness Criteria

ICML

Authors

Published on

07/23/2022

Categories

ICML

Selective regression allows abstention from prediction if the confidence to make an accurate prediction is not sufficient. In general, by allowing a reject option, one expects the performance of a regression model to increase at the cost of reducing coverage (i.e., by predicting on fewer samples). However, as we show, in some cases, the performance of a minority subgroup can decrease while we reduce the coverage, and thus selective regression can magnify disparities between different sensitive subgroups. Motivated by these disparities, we propose new fairness criteria for selective regression requiring the performance of every subgroup to improve with a decrease in coverage. We prove that if a feature representation satisfies the sufficiency criterion or is calibrated for mean and variance, then the proposed fairness criteria is met. Further, we introduce two approaches to mitigate the performance disparity across subgroups: (a) by regularizing an upper bound of conditional mutual information under a Gaussian assumption and (b) by regularizing a contrastive loss for conditional mean and conditional variance prediction. The effectiveness of these approaches is demonstrated on synthetic and real-world datasets.

Please cite our work using the BibTeX below.

@inproceedings{DBLP:conf/icml/ShahBLDPSW22,
  author={Abhin Shah and Yuheng Bu and Joshua K. Lee and Subhro Das and Rameswar Panda and Prasanna Sattigeri and Gregory W. Wornell},
  title={Selective Regression under Fairness Criteria},
  year={2022},
  cdate={1640995200000},
  pages={19598-19615},
  url={https://proceedings.mlr.press/v162/shah22a.html},
  booktitle={ICML},
  crossref={conf/icml/2022}
}
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