This article explores the technical core of AI governance: how to measure and mitigate bias, explain black-box predictions, and ensure robustness. It covers fairness metrics (demographic parity, equalized odds), explainability methods (LIME, SHAP), and adversarial testing, showing how practitioners build accountable systems.