• Corpus ID: 24990444

Data Decisions and Theoretical Implications when Adversarially Learning Fair Representations

@article{Beutel2017DataDA,
  title={Data Decisions and Theoretical Implications when Adversarially Learning Fair Representations},
  author={Alex Beutel and Jilin Chen and Zhe Zhao and Ed H. Chi},
  journal={ArXiv},
  year={2017},
  volume={abs/1707.00075}
}
How can we learn a classifier that is "fair" for a protected or sensitive group, when we do not know if the input to the classifier belongs to the protected group. [] Key MethodHere, we use an adversarial training procedure to remove information about the sensitive attribute from the latent representation learned by a neural network. In particular, we study how the choice of data for the adversarial training effects the resulting fairness properties. We find two interesting results: a small amount of data is…
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