Overview

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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.

Syllabus

Governance and the AI Lifecycle

AI systems move through distinct stages—data acquisition, model training, evaluation, and deployment—but without governance embedded at each stage, critical decisions go undocumented and accountability gaps emerge under regulatory scrutiny. In this module, you examine how to structure AI development as a traceable, governance-integrated pipeline. You map lifecycle stages to governance checkpoints aligned with frameworks like the NIST AI Risk Management Framework and the EU AI Act, and you design responsibility matrices that assign clear ownership for model decisions across technical, risk, and compliance roles. By the end of this module, you will be able to define governance checkpoints for each lifecycle stage and build accountability structures that connect developer work to audit and explainability requirements.

Explainable and Transparent AI

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.

Audit-Ready Documentation Practices

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.