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.