Adversarial Learning of Group and Individual Fair Representations

Fairness is increasingly becoming an important issue in machine learning. Representation learning is a popular approach recently that aims at mitigating discrimination by generating representation on the historical data so that further predictive analysis conducted on the representation is fair. Inspired by this approach, we propose a novel structure, called GIFair, for generating a representation that can simultaneously reconcile utility with both group and individual fairness, compared with most relevant studies that only focus on group fairness. Due to the conflict of the two fairness targets, we need to trade group fairness off against individual fairness in addition to considering the utility of classifiers. To achieve an optimized trade-off performance, we include a focal loss function so that all the targets can receive more balanced attention. Experiments conducted on three real datasets show that GIFair can achieve a better utility-fairness trade-off compared with existing models.