Interpretability and Explainability in AI Using Python

<b><i>Demystify AI Decisions and Master Interpretability and Explainability Today</i></b> <p/><b>Book Description</b><br>Interpretability in AI/ML refers to the ability to understand and explain how a model arrives at its predictions. It ensures that humans can follow the model's reasoning making it easier to debug validate and trust. <p/><b>Interpretability and Explainability in AI Using Python</b> takes you on a structured journey through interpretability and explainability techniques for both white-box and black-box models. <p/>You'll start with foundational concepts in interpretable machine learning exploring different model types and their transparency levels. As you progress you'll dive into post-hoc methods feature effect analysis anchors and counterfactuals-powerful tools to decode complex models. The book also covers explainability in deep learning including Neural Networks Transformers and Large Language Models (LLMs) equipping you with strategies to uncover decision-making patterns in AI systems. <p/>Through hands-on Python examples you'll learn how to apply these techniques in real-world scenarios. By the end you'll be well-versed in choosing the right interpretability methods implementing them efficiently and ensuring AI models align with ethical and regulatory standards-giving you a competitive edge in the evolving AI landscape. <p/><b>Table of Contents</b><br>1. Interpreting Interpretable Machine Learning<br>2. Model Types and Interpretability Techniques<br>3. Interpretability Taxonomy and Techniques<br>4. Feature Effects Analysis with Plots<br>5. Post-Hoc Methods<br>6. Anchors and Counterfactuals<br>7. Interpretability in Neural Networks<br>8. Explainable Neural Networks<br>9. Explainability in Transformers and Large Language Models<br>10. Explainability and Responsible AI<br> <b>Index</b>