Overview

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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.

Syllabus

AI Systems and Responsible AI Foundations

Build a strong conceptual foundation by understanding how AI systems work, how they make decisions, and why Responsible AI is critical in modern applications. This module introduces AI risks, real-world failure cases, and core principles that guide the development of fair, safe, and trustworthy AI systems.

AI Fairness and Transparency

Explore how bias affects AI systems and how fairness and transparency can be achieved through structured evaluation and explainability techniques. This module covers bias types, fairness definitions, and interpretability methods, along with practical approaches to auditing AI systems and improving trust.

AI Risk, Harm, and Accountability

Understand how AI systems create risk and harm, and learn how to manage them using governance, accountability, and control mechanisms. This module focuses on identifying harm, analyzing risk amplification, and applying structured evaluation frameworks to ensure responsible AI deployment.