Explainable artificial intelligence for trustworthy decisions in smart applications / edited by Nicu Bizon and Bhargav Appasani

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TextPublication details: Cham : Springer, 2026Description: xii, 415 pISBN:

9783031970061

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DDC classification:

006.33 EXP-

Summary: This book introduces readers to the field of explainable artificial intelligence (XAI), which aims to make AI models more transparent and trustworthy. It explores how XAI can enhance trust and confidence in AI models and their decisions across various innovative applications in fields such as healthcare, finance, and engineering, where AI can significantly impact quality of life. Readers will discover emerging trends related to XAI—such as large language models, generative AI, and natural language processing—that are transforming the landscape of AI research and applications. Featuring an interdisciplinary overview, the book examines the state of the art, challenges, and opportunities in XAI, accompanied by clear examples and detailed explanations of its methods and techniques. The book also offers a balanced perspective on the limitations and trade-offs of XAI and outlines future directions and opportunities for both research and practice. This book is intended for anyone who wants to learn more about XAI and understand how it can enhance trust in AI models

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Books

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Current library	Call number	Status	Barcode
UMT Main Campus	006.33 EXP- (Browse shelf(Opens below))	Available	152962

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Previous	006.33 BOW-P Prolog and expert systems	006.33 BRE-I Intelligent software agents : Foundations and applications	006.33 DIS- Distributed artificial intelligence for 5G/6G communications : frameworks with machine learning /	006.33 EXP- Explainable artificial intelligence for trustworthy decisions in smart applications /	006.33 FUZ- Fuzzy expert system tools	006.33 GON-E The Engineering of knowledge-based systems:	006.33 GON-E The Engineering of knowledge-based systems:	Next

Includes bibliographical references.

This book introduces readers to the field of explainable artificial intelligence (XAI), which aims to make AI models more transparent and trustworthy. It explores how XAI can enhance trust and confidence in AI models and their decisions across various innovative applications in fields such as healthcare, finance, and engineering, where AI can significantly impact quality of life. Readers will discover emerging trends related to XAI—such as large language models, generative AI, and natural language processing—that are transforming the landscape of AI research and applications. Featuring an interdisciplinary overview, the book examines the state of the art, challenges, and opportunities in XAI, accompanied by clear examples and detailed explanations of its methods and techniques. The book also offers a balanced perspective on the limitations and trade-offs of XAI and outlines future directions and opportunities for both research and practice. This book is intended for anyone who wants to learn more about XAI and understand how it can enhance trust in AI models

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