Learn to govern AI systems with accountability, transparency, auditability, and risk-based oversight. This course introduces AI governance across the lifecycle, from assigning organizational responsibilities and human oversight to documenting decisions and tracing data lineage.

Designed for professionals in data governance, risk, audit, compliance, and AI operations, the course helps you assess governance evidence and identify gaps in AI oversight. You'll distinguish continuous model monitoring from independent audits, training-data assessments, and security reviews. Explore explainable AI, algorithmic transparency, bias detection, and fairness metrics to evaluate model decisions in context. You'll also examine the NIST AI Risk Management Framework (AI RMF), the EU AI Act's risk-based approach, and OECD AI Principles to identify governance actions and propose accountable responses to AI risk scenarios. What sets this course apart is its integrated focus on lifecycle responsibilities, audit controls, fairness evaluation, and regulatory frameworks. Enroll to strengthen your ability to map oversight checkpoints, evaluate transparency, compare fairness approaches, and communicate responsible AI governance controls. This course is educational and does not provide legal advice or certify regulatory compliance.