Detecting and Mitigating Bias by Treating Fairness as a Symmetry Operation

This research formalizes bias in machine learning systems as a symmetry-breaking operation, defining fairness as invariance under counterfactual sensitive attribute switching. It implements loss-based regularization as a symmetry-restoring mechanism, achieving over 90% bias violation reduction with approximately 5% accuracy cost.