Data Augmentation via Subgroup Mixup for Improving Fairness
Madeline Navarro Camille Little Genevera I. Allen Santiago Segarra
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.