Input-agnostic Certified Group Fairness via Gaussian Parameter Smoothing. (arXiv:2206.11423v1 [cs.LG])

Web: http://arxiv.org/abs/2206.11423

Only recently, researchers attempt to provide classification algorithms with
provable group fairness guarantees. Most of these algorithms suffer from
harassment caused by the requirement that the training and deployment data
follow the same distribution. This paper proposes an input-agnostic certified
group fairness algorithm, FairSmooth, for improving the fairness of
classification models while maintaining the remarkable prediction accuracy. A
Gaussian parameter smoothing method is developed to transform base classifiers
into their smooth versions. An optimal individual smooth classifier is learnt
for …

arxiv fairness group lg