Data Augmentation via Subgroup Mixup for Improving Fairness
It addresses fairness biases in ML systems for applications affected by societal or data imbalances, but is incremental as it builds on existing mixup techniques.
The paper tackles group fairness in machine learning by proposing a pairwise mixup data augmentation method across subgroups to balance underrepresented populations, achieving fair outcomes with robust or improved accuracy on synthetic and real-world benchmarks.
In this work, we propose data augmentation via pairwise mixup across subgroups to improve group fairness. Many real-world applications of machine learning systems exhibit biases across certain groups due to under-representation or training data that reflects societal biases. Inspired by the successes of mixup for improving classification performance, we develop a pairwise mixup scheme to augment training data and encourage fair and accurate decision boundaries for all subgroups. Data augmentation for group fairness allows us to add new samples of underrepresented groups to balance subpopulations. Furthermore, our method allows us to use the generalization ability of mixup to improve both fairness and accuracy. We compare our proposed mixup to existing data augmentation and bias mitigation approaches on both synthetic simulations and real-world benchmark fair classification data, demonstrating that we are able to achieve fair outcomes with robust if not improved accuracy.