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Fair Classification under Covariate Shift and Missing Protected Attribute -- an Investigation using Related Features

arXiv 2204.07987 ↗First posted 17 Apr 2022cs.LG cs.CYcited by 0

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Abstract (arXiv)

This study investigated the problem of fair classification under Covariate Shift and missing protected attribute using a simple approach based on the use of importance-weights to handle covariate-shift and, Related Features arXiv:2104.14537 to handle missing protected attribute.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 6 of its 16 references that are in the map. First posted on arXiv 17 Apr 2022.

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6 of 16 references.

References · 6

  1. 2016Equality of Opportunity in Supervised Learningcited by 2,007
  2. 2018A Reductions Approach to Fair Classificationcited by 587
  3. 2020Fairness without Demographics through Adversarially Reweighted Learningcited by 251
  4. 2015The Variational Fair Autoencodercited by 223
  5. 2020Robust Fairness under Covariate Shiftcited by 58
  6. 2019Transfer of Machine Learning Fairness across Domainscited by 36

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