Foundations of AI Governance and Responsible Development

This course introduces the foundational practices required to design, develop, and manage AI systems responsibly in regulated and high-stakes environments. Learners explore how to integrate governance into every stage of the AI lifecycle, ensuring that models are transparent, accountable, and audit-ready from development through deployment and monitoring. The course emphasizes building structured governance checkpoints, defining clear accountability using frameworks like RACI, and aligning technical workflows with regulatory expectations such as the NIST AI Risk Management Framework and the EU AI Act. Learners will also develop practical skills in explainable AI, applying techniques like SHAP and LIME to generate reliable, instance-level insights and communicate them effectively to stakeholders, including regulators, executives, and customers. In addition, the course covers audit-ready documentation practices, including model traceability, version control, and the creation of structured audit reports that synthesize lifecycle evidence into governance-ready artifacts. By the end of the course, learners will be able to design AI systems that not only perform well technically but also withstand compliance review, support risk management, and build organizational trust.