Interpretability and Explainability in AI Using Python: Decrypt AI Decision-Making Using Interpretability and Explainability with Python to Build Reliable Machine Learning Systems

Chakkirala, Aruna; AVA, Orange

ISBN 10: 9348107577 / ISBN 13: 9789348107572

Published by Orange Education Pvt Ltd, 2025

About this Item

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Bibliographic Details

• Title: Interpretability and Explainability in AI Using Python: Decrypt AI Decision-Making Using Interpretability and Explainability with Python to Build Reliable Machine Learning Systems
• Publisher: Orange Education Pvt Ltd
• Publication Date: 2025
• Language: English
• ISBN 10: 9348107577
• ISBN 13: 9789348107572
• Binding: Soft cover
• Condition: New

About this title

Synopsis

Demystify AI Decisions and Master Interpretability and Explainability Today

Book Description
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.

Interpretability and Explainability in AI Using Python takes you on a structured journey through interpretability and explainability techniques for both white-box and black-box models.

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.

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.

Table of Contents
1. Interpreting Interpretable Machine Learning
2. Model Types and Interpretability Techniques
3. Interpretability Taxonomy and Techniques
4. Feature Effects Analysis with Plots
5. Post-Hoc Methods
6. Anchors and Counterfactuals
7. Interpretability in Neural Networks
8. Explainable Neural Networks
9. Explainability in Transformers and Large Language Models
10. Explainability and Responsible AI
Index