A new framework for detecting and mitigating bias in machine learning systems treats fairness as a symmetry operation, achieving over 90% reduction in bias violations with minimal accuracy loss. This approach is computationally efficient and applicable to various sensitive attributes, making it valuable for addressing discrimination in AI applications.