Foundations of Interpretability

Chapter 1: Introduction to Interpretability Importance, history, and challenges of interpretable AI [Black-box models, stakeholder needs, trust in AI] Chapter 2: Goals of Interpretability Transparency, trust, debugging, fairness, and regulatory compliance [Explainability vs. interpretability, user-centric design, societal impact] Chapter 3: Data and Models for Interpretability Role of datasets, model complexity, and preprocessing in enabling interpretability [Feature engineering, data biases, model selection]