• Corpus ID: 246634065

Fair Interpretable Representation Learning with Correction Vectors

@article{Cerrato2022FairIR,
  title={Fair Interpretable Representation Learning with Correction Vectors},
  author={Mattia Cerrato and A Coronel and Marius K{\"o}ppel and Alexander Segner and Roberto Esposito and Stefan Kramer},
  journal={ArXiv},
  year={2022},
  volume={abs/2202.03078}
}
Neural network architectures have been extensively employed in the fair representation learning setting, where the objective is to learn a new representation for a given vector which is independent of sensitive information. Various representation debiasing techniques have been proposed in the literature. However, as neural networks are inherently opaque, these methods are hard to comprehend, which limits their usefulness. We propose a new framework for fair representation learning that is… 
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2 Citations

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