Hierarchical VampPrior Variational Fair Auto-Encoder
By Philip Botros, J. Tomczak. 2018
7 citations recorded by the source corpus.
Decision making is a process that is extremely prone to different biases. In this paper we consider learning fair representations that aim at removing nuisance (sensitive) information from the decision process. For this purpose, we propose to use deep generative modeling and adapt a hierarchical Variational Auto-Encoder to learn these fair representations. Moreover, we utilize the mutual information as a useful regularizer for enforcing fairness of a representation. In experiments on two benchmark datasets and two scenarios where the sensitive variables are fully and partially observable, we show that the proposed approach either outperforms or performs on par with the current best model.
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- Elliot Creager
- Richard S. Zemel
- Jörn-Henrik Jacobsen
- Yoshua Bengio
- David Madras
- Kevin Swersky
- Han-Jia Ye
- Ruslan Salakhutdinov
Citations in the corpus
- Flexibly Fair Representation Learning by Disentanglement (2019, background): “Several recent VAE variants have incorporated label information into latent vari-Flexibly able learning for image synthesis (Klys et al., 2018) and single-attribute fair representation learning (Song et al., 2019; Botros & Tomczak, 2018; Moyer et al., 2018).”