Unit 0

Why Explainable AI?

AI already filters spam, routes traffic, detects fraud, recommends what we watch, and helps people make consequential decisions. This unit asks what happens when the model is accurate for the wrong reason, who needs to understand it, and what useful understanding looks like in practice.

Chapter 1

Why is this book called "Explainable" AI?

You will be able to describe a black-box model, follow data as it becomes a numerical representation, distinguish transparency, interpretability, and explainability, and understand why this book uses Explainable AI as an umbrella term.

1. What's a "Black Box"?→
2. Transparency, Interpretability, Explainability→
3. A Note on This Book's Name→

Chapter 2

Why Do We Need Explainable AI?

You will examine six practical reasons to understand an AI system: finding uneven failures, calibrating trust, establishing accountability, testing reliability, debugging shortcuts, and designing effective human oversight.

1. Bias and Fairness→
2. Trust and Adoption→
3. Accountability→
4. Safety and Reliability→
5. Debugging and Model Improvement→
7. Human-AI Collaboration→