Module 9: AI Risk & Governance - Bias, Explainability, and the Regulatory Landscape

How does an executive defend an AI deployment to a regulator, a board, or a journalist?

AI risk is now a board-level topic, and the people answering board questions need real frameworks rather than vendor talking points. Bias, explainability, robustness, and regulatory exposure each have specific testable properties. This module gives you the working vocabulary and the controls that actually defend an AI deployment.

What you'll learn in this module

• How bias enters AI systems through data, modeling choices, and deployment context, and the metrics that surface each kind
• Explainability versus interpretability, what each technique (LIME, SHAP, counterfactuals) actually shows, and where they mislead
• The 2026 regulatory landscape: EU AI Act tiered obligations, NIST AI RMF, ISO 42001, sector-specific regimes
• The governance operating model: who signs off on a model going to production, what the documentation pack looks like, who owns post-deployment monitoring
• Incident response: how to respond to an AI-driven failure publicly, internally, and with the regulator

The complete module hands executives the structured posture they need to govern AI without slowing it to a halt or letting it deploy unsupervised.