In this module, you will explore the methods and governance practices that make machine learning models explainable and transparent to the people who oversee, audit, and are affected by them. You will examine how post-hoc techniques such as SHAP and LIME assign attribution to individual predictions, and why the distinction between global and local explanations matters for regulated decision-making. You will also examine how raw technical outputs from these methods must be translated into artifacts that satisfy compliance requirements and communicate meaningfully to risk committees, regulators, and business leaders. By the end of this module, you will be able to implement and validate an explainability pipeline, interpret its outputs for diverse audiences, and integrate those outputs into governance and compliance workflows.

In this module, you focus on the documentation practices that make AI systems auditable in real-world corporate environments. You examine how to establish traceability across models, data, and configurations so that any decision can be reconstructed with confidence. You also learn how to structure audit-ready reports that translate technical evidence into governance artifacts aligned with regulatory expectations. These practices are critical when systems are reviewed by internal audit, regulators, or risk committees. By the end of this module, you will be able to design traceable AI documentation systems and produce structured audit reports that support compliance, accountability, and operational decision-making.