Foundations of Explainable AI (XAI)

As machine learning models become more complex, understanding how they arrive at specific decisions is crucial for building trust, ensuring fairness, and meeting regulatory standards. This text-based course guides you through the core concepts of Explainable AI (XAI), helping you transition from treating models as black boxes to thoroughly understanding their inner workings. What you'll learn: • Understand the fundamental differences between interpretability and explainability in machine learning. • Explore global and local explanation methods to analyze model behavior at different scales. • Learn the mechanics of key XAI frameworks like SHAP and LIME. • Identify and mitigate bias in training data and model predictions using transparency tools. • Apply ethical AI principles to design systems that are accountable and compliant with modern regulations. • Practice evaluating explanation quality through conceptual written exercises. You will start with essential terminology and the philosophical foundations of model transparency before moving into practical interpretability techniques and real-world ethical considerations. This course is designed for beginners, data enthusiasts, and aspiring AI practitioners who want to understand model transparency without needing advanced programming prerequisites. Start reading today to unlock the black box of machine learning and build explainable AI systems.