Renyi Fair Information Bottleneck for Image Classification [article]

Adam Gronowski and William Paul and Fady Alajaji and Bahman Gharesifard and Philippe Burlina
2022 arXiv   pre-print
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 the
more » ... rmance of our method for image classification using the EyePACS medical imaging dataset, showing it outperforms competing state of the art techniques with performance measured using a variety of compound utility/fairness metrics, including accuracy gap and Rawls' minimal accuracy.
arXiv:2203.04950v2 fatcat:4c7oq4wuyffwbig4rwipepnsnm