Example Concept: Organizations should adopt a bias mitigation framework that includes regular bias audits, the use of fairness metrics (e.g., demographic parity, equalized odds), and the implementation of bias correction techniques. This process should be guided by ethical AI principles and involve diverse stakeholder engagement to ensure that AI systems do not disproportionately impact any group.