Explainable AI for Everyone

This program explores how Explainable AI (XAI) enables practitioners to understand, interpret, and communicate machine learning model behavior with clarity and confidence. You’ll begin by learning the foundational principles of explainability, including interpretability, transparency, and the taxonomy of explanation methods. Through hands-on activities, you will explore how different types of explanations apply to real-world models and how inherently interpretable models such as linear models and decision trees provide direct insight into model behavior. You’ll then dive into post-hoc explanation techniques that help interpret complex and black-box models. You will learn the difference between model-agnostic and model-specific methods and apply techniques such as permutation importance, Partial Dependence Plots (PDP), and Individual Conditional Expectation (ICE) to analyze global feature effects. Practical demonstrations will guide you through implementing these methods, visualizing model behavior, and interpreting patterns that influence predictions. Next, you’ll explore local explanation techniques, focusing on understanding individual predictions using LIME and SHAP. You will learn how surrogate models approximate local behavior and how Shapley values provide a theoretically grounded approach to feature attribution. Hands-on exercises will help you generate and interpret both global and local SHAP insights, enabling deeper understanding of model decisions at multiple levels. Finally, you’ll examine the critical aspects of trust, fairness, and communication in Explainable AI. You will learn how bias emerges in machine learning systems, how to evaluate fairness using practical tools, and how to balance accuracy with interpretability. You will also design clear and effective explanation reports, using visual and narrative techniques to communicate insights to both technical and non-technical stakeholders. By the end of this program, you will be able to: - Expla