Fair Ranking with Noisy Protected Attributes

@article{Mehrotra2022FairRW,
  title={Fair Ranking with Noisy Protected Attributes},
  author={Anay Mehrotra and Nisheeth K. Vishnoi},
  journal={ArXiv},
  year={2022},
  volume={abs/2211.17067}
}
The fair-ranking problem, which asks to rank a given set of items to maximize utility subject to group fairness constraints, has received attention in the fairness, information retrieval, and machine learning literature. Recent works, however, observe that errors in socially-salient (including protected) attributes of items can significantly undermine fairness guarantees of existing fair-ranking algorithms and raise the problem of mitigating the effect of such errors. We study the fairranking… 
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