Responsible AI

This course introduces the foundations and practical implementation of AI governance, helping organizations design and manage responsible AI systems. You’ll begin by understanding core governance concepts, stakeholder roles, and how governance differs from ethics and compliance. The course then explores global frameworks such as the EU AI Act and NIST AI RMF, enabling you to align AI systems with regulatory expectations. Next, you’ll learn how to operationalize governance through policy design, maturity models, and lifecycle risk management using tools like risk registers and impact assessments. The course also covers monitoring, auditing, and incident response to ensure continuous oversight of AI systems. By the end of this course, you will be able to: - Explain AI governance fundamentals and stakeholder roles - Apply global frameworks to real-world AI systems - Design policies and manage lifecycle risks - Monitor, audit, and respond to AI risks Designed for professionals, analysts, and anyone working with AI systems, this course provides a structured approach to implementing AI governance in practice. To be successful, learners should have a basic understanding of AI concepts and business processes. Start your journey into responsible AI and learn how to build governance systems that ensure accountability and trust.

This course introduces the foundations of Responsible AI, helping learners understand how AI systems make decisions, where risks emerge, and how organizations can build trustworthy and accountable AI solutions. The course explores AI fairness, bias, transparency, explainability, accountability, and human oversight through practical examples and hands-on activities. You’ll also examine AI risks, harms, feedback loops, and operational controls used to support responsible AI deployment in real-world systems. By the end of this course, you will be able to: - Explain how AI systems generate predictions and decisions in real-world applications - Identify key Responsible AI principles, including fairness, transparency, accountability, and oversight - Analyze AI risks, harms, and feedback loops across the AI system lifecycle - Evaluate algorithmic bias and fairness trade-offs using practical auditing techniques - Apply transparency and explainability practices using model cards and AI documentation This course is designed for AI practitioners, data professionals, business leaders, governance teams, compliance professionals, and technology learners who want to understand how to build, evaluate, and manage trustworthy AI systems. A basic understanding of AI or machine learning concepts will help maximize your learning experience, though no advanced technical background is required. Learners need a reliable internet connection, a modern web browser, and access to standard productivity and AI learning tools; no specialized hardware is required. Join us to explore Responsible AI and learn how to design, evaluate, and govern AI systems that are fair, transparent, accountable, and trustworthy.