FairNN- Conjoint Learning of Fair Representations for Fair Decisions

@inproceedings{Hu2020FairNNCL,
  title={FairNN- Conjoint Learning of Fair Representations for Fair Decisions},
  author={Tongxin Hu and Vasileios Iosifidis and Wentong Liao and Hang Zhang and M. Yang and Eirini Ntoutsi and B. Rosenhahn},
  booktitle={DS},
  year={2020}
}
In this paper, we propose FairNN a neural network that performs joint feature representation and classification for fairness-aware learning. Our approach optimizes a multi-objective loss function in which (a) learns a fair representation by suppressing protected attributes (b) maintains the information content by minimizing a reconstruction loss and (c) allows for solving a classification task in a fair manner by minimizing the classification error and respecting the equalized odds-based… Expand
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