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We develop a novel method for ensuring fairness in machine learning which we term as the Renyi Fair Information Bottleneck (RFIB). We consider two different fairness constraints - demographic parity and equalized odds - for learning fair representations and derive a loss function via a variational approach that uses Renyi's divergence with its tunable parameter α and that takes into account the triple constraints of utility, fairness, and compactness of representation. We then evaluate thearXiv:2203.04950v2 fatcat:4c7oq4wuyffwbig4rwipepnsnm