Trustworthy AI: Explainability, Fairness, and Regulatory Compliance

As Artificial Intelligence systems become ubiquitous in high-stakes decision-making, ensuring their trustworthiness is paramount. This specialized course provides the tools and frameworks needed to build, deploy, and govern AI systems that are fair, transparent, and compliant with emerging global regulations. It provides a deep technical dive into Explainable AI (XAI) methods, fairness metrics, and audit protocols, moving beyond high-level principles to practical implementation. Participants will learn how to operationalize trustworthiness across the entire Machine Learning lifecycle, from data preparation to continuous monitoring in production.