• Corpus ID: 222290556

Robust Fairness under Covariate Shift

@inproceedings{Rezaei2021RobustFU,
  title={Robust Fairness under Covariate Shift},
  author={Ashkan Rezaei and Anqi Liu and Omid Memarrast and Brian D. Ziebart},
  booktitle={AAAI},
  year={2021}
}
Making predictions that are fair with regard to protected group membership (race, gender, age, etc.) has become an important requirement for classification algorithms. Existing techniques derive a fair model from sampled labeled data relying on the assumption that training and testing data are identically and independently drawn (iid) from the same this http URL practice, distribution shift can and does occur between training and testing datasets as the characteristics of individuals… 
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Methods Citations
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