  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.
