Evaluating Explainable AI with Feature Agreement Metrics

As machine learning models grow more complex, understanding why they make specific decisions is crucial, yet we must ensure our explanations themselves are actually trustworthy. This course teaches you how to rigorously evaluate explainable AI (XAI) techniques for image classifiers using standard faithfulness and feature agreement metrics. What you will learn: 1. Understand the fundamentals of explainable AI (XAI) and post-hoc explanation algorithms. 2. Define ground-truth faithfulness and why it is critical for trustworthy machine learning. 3. Calculate Top-k feature agreement to measure overlap between model explanations and known important features. 4. Apply rank correlation metrics to evaluate the consistency of attribution methods. 5. Analyze common failure modes of popular explanation algorithms on image classification tasks. 6. Explore modern evaluation frameworks and best practices for selecting reliable baseline models. This text-based course begins with core interpretability concepts and foundational definitions before guiding you through the mathematical formulations of faithfulness metrics. Through clear written explanations and structured analytical exercises, you will learn how to audit model explanations objectively. This course is designed for beginner to intermediate data scientists, machine learning practitioners, and AI enthusiasts looking to make their models more transparent; no prior experience with explainability metrics is required. Start building more transparent and reliable machine learning systems today.