Software Fairness Analysis and Repair via Causal Model-Guided Data Mutation
This work introduces Fairabel, a novel approach that repairs fairness by mutating the training data under the guidance of causal models and multi-objective optimization, and shows that Fairabel reduces ML software bias by 50% on average across three fairness metrics, outperforming the state-of-the-art with a 9% relativ...