Integrating Fair Representation Learning with Fairness Regularization for Intersectional Group Fairness

In the pursuit of intersectional group fairness in machine learning models, significant attention has been directed towards fair representation learning methods. These methods aim to mitigate bias in training data by encoding data effectively while removing sensitive attribute information. However, existing fair representation learning methods often assume that decoupling sensitive attribute information from the latent representation will automatically lead to fairness on any downstream tasks learnt on the non-sensitive subspace of the latent representation. Nonetheless, biases can persist even when using representations devoid of sensitive attribute information. This is due to the learning algorithm's influence during downstream task training. In this paper, we propose a method dubbed FairReg which integrates fairness regularization with fair representation learning. This unified approach creates a more comprehensive and robust framework for ensuring intersectional group fairness in machine learning models. Empirical evaluations conducted on two real-world depression prediction datasets demonstrate the effectiveness of our method in improving intersectional group fairness compared to existing approaches.