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There are many excellent books on explainable AI that provide insights into the challenges and techniques used to create transparent and interpretable machine learning models. Here are some of the best explainable AI books:

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By Stuart Russell

This book explores the challenges of creating safe and trustworthy AI systems that align with human values. It covers topics such as value alignment, reward engineering, and the control problem.

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By Christoph Molnar

This book provides a comprehensive overview of the methods and tools used to create interpretable machine learning models. It covers topics such as feature importance, partial dependence plots, and model-agnostic techniques.

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By Gary Smith

This book provides a critical analysis of the hype surrounding AI and its limitations. It covers topics such as the limitations of machine learning, the dangers of overreliance on data, and the importance of human judgment.

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By Markus Christen, Andreas Huppenkothen, and Bernhard Nebel

This book provides a collection of essays on the ethical and societal implications of AI. It covers topics such as bias, fairness, transparency, and accountability.

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By Springer Nature

This book provides an in-depth exploration of explainable AI techniques for deep learning models. It covers topics such as saliency maps, occlusion sensitivity, and adversarial examples.

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