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Explainable AI with Python

Record Type:	Electronic resources : Monograph/item
Title/Author:	Explainable AI with Python/ by Antonio Di Cecco, Leonida Gianfagna.
Author:	Di Cecco, Antonio.
other author:	Gianfagna, Leonida.
Published:	Cham :Springer Nature Switzerland : : 2025.,
Description:	xx, 324 p. :ill. (chiefly col.), digital ;24 cm.
[NT 15003449]:	Chapter 1 The Landscape -- Chapter 2 "Explainable AI: needs, opportunities and challenges" -- Chapter 3 Intrinsic Explainable Models -- Chapter 4 Model-agnostic methods for XAI -- Chapter 5 Explaining Deep Learning Models -- Chapter 6 Additive Models for Interpretability -- Chapter 7 Adversarial Machine Learning and Explainability -- Chapter 8 Explainability of Language Models (XAI and LLM) -- Chapter 9 Making science with Machine Learning and XAI -- Chapter 10 AGI, LLM, XAI -- Chapter 11 "A proposal for a sustainable model of Explainable AI.
Contained By:	Springer Nature eBook
Subject:	Artificial intelligence. -
Online resource:	https://doi.org/10.1007/978-3-031-92229-9
ISBN:	9783031922299

Explainable AI with Python
Di Cecco, Antonio.

Explainable AI with Python[electronic resource] /by Antonio Di Cecco, Leonida Gianfagna. - Second edition. - Cham :Springer Nature Switzerland :2025. - xx, 324 p. :ill. (chiefly col.), digital ;24 cm.

Chapter 1 The Landscape -- Chapter 2 "Explainable AI: needs, opportunities and challenges" -- Chapter 3 Intrinsic Explainable Models -- Chapter 4 Model-agnostic methods for XAI -- Chapter 5 Explaining Deep Learning Models -- Chapter 6 Additive Models for Interpretability -- Chapter 7 Adversarial Machine Learning and Explainability -- Chapter 8 Explainability of Language Models (XAI and LLM) -- Chapter 9 Making science with Machine Learning and XAI -- Chapter 10 AGI, LLM, XAI -- Chapter 11 "A proposal for a sustainable model of Explainable AI.

This comprehensive book has been updated and expanded to reflect the latest advancements in the field of XAI, enriching the existing literature with new research, case studies, and practical techniques. The expanded Second Edition addresses advancements in AI including LLMs and multimodal systems that integrate text, visual, auditory, and sensor data. It emphasizes making complex systems interpretable without sacrificing performance and provides an enhanced focus on additive models for improved interpretability. Balancing technical rigor with accessibility, the book combines theory and practical application to equip readers with the skills needed to apply explainable AI (XAI) methods effectively in real-world contexts. Features: Expanded "Intrinsic Explainable Models" Chapter - Now includes a deeper exploration of generalized additive models and other intrinsic techniques, with new examples and use cases for better understanding. Enhanced "Model-Agnostic Methods for XAI" - Focuses on how explanations vary between the training and test sets, introducing a new model to illustrate these differences more clearly and effectively. New Section in "Making Science with Machine Learning and XAI" - Presents a visual approach to learning fundamental XAI functions, making concepts more accessible through an interactive and engaging interface. Revised "Adversarial Machine Learning and Explainability" Chapter - Features a comprehensive code review to improve clarity and effectiveness, ensuring examples align with current best practices. New Chapter on "Generative Models and Large Language Models (LLMs)" - Explores the role of generative and large language models in XAI, covering the explainability of transformer models, and privacy considerations. New "Artificial General Intelligence and XAI" Chapter - Examines implications of Artificial General Intelligence (AGI) on XAI, and how advancements toward AGI systems shape explainability strategies and methodologies. Updated "Explaining Deep Learning Models" Chapter - Introduces new methodologies for explaining deep learning models, incorporating cutting-edge techniques and insights for a deeper understanding.

ISBN: 9783031922299

Standard No.: 10.1007/978-3-031-92229-9doiSubjects--Topical Terms:

516317
Artificial intelligence.

LC Class. No.: Q325.5

Dewey Class. No.: 006.31

Explainable AI with Python
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100 1 $a Di Cecco, Antonio. $3 3493957
245 1 0 $a Explainable AI with Python $h [electronic resource] / $c by Antonio Di Cecco, Leonida Gianfagna.
250 $a Second edition.
260 $a Cham : $b Springer Nature Switzerland : $b Imprint: Springer, $c 2025.
300 $a xx, 324 p. : $b ill. (chiefly col.), digital ; $c 24 cm.
505 0 $a Chapter 1 The Landscape -- Chapter 2 "Explainable AI: needs, opportunities and challenges" -- Chapter 3 Intrinsic Explainable Models -- Chapter 4 Model-agnostic methods for XAI -- Chapter 5 Explaining Deep Learning Models -- Chapter 6 Additive Models for Interpretability -- Chapter 7 Adversarial Machine Learning and Explainability -- Chapter 8 Explainability of Language Models (XAI and LLM) -- Chapter 9 Making science with Machine Learning and XAI -- Chapter 10 AGI, LLM, XAI -- Chapter 11 "A proposal for a sustainable model of Explainable AI.
520 $a This comprehensive book has been updated and expanded to reflect the latest advancements in the field of XAI, enriching the existing literature with new research, case studies, and practical techniques. The expanded Second Edition addresses advancements in AI including LLMs and multimodal systems that integrate text, visual, auditory, and sensor data. It emphasizes making complex systems interpretable without sacrificing performance and provides an enhanced focus on additive models for improved interpretability. Balancing technical rigor with accessibility, the book combines theory and practical application to equip readers with the skills needed to apply explainable AI (XAI) methods effectively in real-world contexts. Features: Expanded "Intrinsic Explainable Models" Chapter - Now includes a deeper exploration of generalized additive models and other intrinsic techniques, with new examples and use cases for better understanding. Enhanced "Model-Agnostic Methods for XAI" - Focuses on how explanations vary between the training and test sets, introducing a new model to illustrate these differences more clearly and effectively. New Section in "Making Science with Machine Learning and XAI" - Presents a visual approach to learning fundamental XAI functions, making concepts more accessible through an interactive and engaging interface. Revised "Adversarial Machine Learning and Explainability" Chapter - Features a comprehensive code review to improve clarity and effectiveness, ensuring examples align with current best practices. New Chapter on "Generative Models and Large Language Models (LLMs)" - Explores the role of generative and large language models in XAI, covering the explainability of transformer models, and privacy considerations. New "Artificial General Intelligence and XAI" Chapter - Examines implications of Artificial General Intelligence (AGI) on XAI, and how advancements toward AGI systems shape explainability strategies and methodologies. Updated "Explaining Deep Learning Models" Chapter - Introduces new methodologies for explaining deep learning models, incorporating cutting-edge techniques and insights for a deeper understanding.
650 0 $a Artificial intelligence. $3 516317
650 0 $a Python (Computer program language) $3 729789
650 1 4 $a Artificial Intelligence. $3 769149
650 2 4 $a Machine Learning. $3 3382522
650 2 4 $a Python. $3 3201289
700 1 $a Gianfagna, Leonida. $3 3493956
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer Nature eBook
856 4 0 $u https://doi.org/10.1007/978-3-031-92229-9
950 $a Professional and Applied Computing (SpringerNature-12059)