Example Concept: AI-driven bias mitigation involves deploying fairness evaluation techniques such as disparate impact analysis, demographic parity, and equalized odds. Organizations should regularly audit AI models for bias using these metrics and adjust models or decision thresholds to reduce unfair outcomes. Governance frameworks guide the establishment of accountability and transparency in AI systems.