Related papers: Fairness without the sensitive attribute via Causal Variational Autoencoder

Fairness without the sensitive attribute via Causal Variational Autoencoder

URL: http://arxiv.org/abs/2109.04999v1
Date: Fri, 10 Sep 2021 17:12:52 GMT
Title: Fairness without the sensitive attribute via Causal Variational Autoencoder
Authors: Vincent Grari, Sylvain Lamprier, Marcin Detyniecki
Abstract summary: Due to privacy purposes and var-ious regulations such as RGPD in EU, many personal sensitive attributes are frequently not collected. By leveraging recent developments for approximate inference, we propose an approach to fill this gap. Based on a causal graph, we rely on a new variational auto-encoding based framework named SRCVAE to infer a sensitive information proxy.
Score: 17.675997789073907
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: In recent years, most fairness strategies in machine learning models focus on mitigating unwanted biases by assuming that the sensitive information is observed. However this is not always possible in practice. Due to privacy purposes and var-ious regulations such as RGPD in EU, many personal sensitive attributes are frequently not collected. We notice a lack of approaches for mitigating bias in such difficult settings, in particular for achieving classical fairness objectives such as Demographic Parity and Equalized Odds. By leveraging recent developments for approximate inference, we propose an approach to fill this gap. Based on a causal graph, we rely on a new variational auto-encoding based framework named SRCVAE to infer a sensitive information proxy, that serve for bias mitigation in an adversarial fairness approach. We empirically demonstrate significant improvements over existing works in the field. We observe that the generated proxy's latent space recovers sensitive information and that our approach achieves a higher accuracy while obtaining the same level of fairness on two real datasets, as measured using com-mon fairness definitions.

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