Explainable Ai Interpreting Explaining And Visualizing Deep Learning
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Explainable Ai Interpreting Explaining And Visualizing Deep Learning
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Author : Wojciech Samek
language : en
Publisher: Springer Nature
Release Date : 2019-09-10
Explainable Ai Interpreting Explaining And Visualizing Deep Learning written by Wojciech Samek and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-09-10 with Computers categories.
The development of “intelligent” systems that can take decisions and perform autonomously might lead to faster and more consistent decisions. A limiting factor for a broader adoption of AI technology is the inherent risks that come with giving up human control and oversight to “intelligent” machines. For sensitive tasks involving critical infrastructures and affecting human well-being or health, it is crucial to limit the possibility of improper, non-robust and unsafe decisions and actions. Before deploying an AI system, we see a strong need to validate its behavior, and thus establish guarantees that it will continue to perform as expected when deployed in a real-world environment. In pursuit of that objective, ways for humans to verify the agreement between the AI decision structure and their own ground-truth knowledge have been explored. Explainable AI (XAI) has developed as a subfield of AI, focused on exposing complex AI models to humans in a systematic and interpretable manner. The 22 chapters included in this book provide a timely snapshot of algorithms, theory, and applications of interpretable and explainable AI and AI techniques that have been proposed recently reflecting the current discourse in this field and providing directions of future development. The book is organized in six parts: towards AI transparency; methods for interpreting AI systems; explaining the decisions of AI systems; evaluating interpretability and explanations; applications of explainable AI; and software for explainable AI.
Explainable Artificial Intelligence An Introduction To Interpretable Machine Learning
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Author : Uday Kamath
language : en
Publisher: Springer Nature
Release Date : 2021-12-15
Explainable Artificial Intelligence An Introduction To Interpretable Machine Learning written by Uday Kamath and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-12-15 with Computers categories.
This book is written both for readers entering the field, and for practitioners with a background in AI and an interest in developing real-world applications. The book is a great resource for practitioners and researchers in both industry and academia, and the discussed case studies and associated material can serve as inspiration for a variety of projects and hands-on assignments in a classroom setting. I will certainly keep this book as a personal resource for the courses I teach, and strongly recommend it to my students. --Dr. Carlotta Domeniconi, Associate Professor, Computer Science Department, GMU This book offers a curriculum for introducing interpretability to machine learning at every stage. The authors provide compelling examples that a core teaching practice like leading interpretive discussions can be taught and learned by teachers and sustained effort. And what better way to strengthen the quality of AI and Machine learning outcomes. I hope that this book will become a primer for teachers, data Science educators, and ML developers, and together we practice the art of interpretive machine learning. --Anusha Dandapani, Chief Data and Analytics Officer, UNICC and Adjunct Faculty, NYU This is a wonderful book! I’m pleased that the next generation of scientists will finally be able to learn this important topic. This is the first book I’ve seen that has up-to-date and well-rounded coverage. Thank you to the authors! --Dr. Cynthia Rudin, Professor of Computer Science, Electrical and Computer Engineering, Statistical Science, and Biostatistics & Bioinformatics Literature on Explainable AI has up until now been relatively scarce and featured mainly mainstream algorithms like SHAP and LIME. This book has closed this gap by providing an extremely broad review of various algorithms proposed in the scientific circles over the previous 5-10 years. This book is a great guide to anyone who is new to the field of XAI or is already familiar with the field and is willing to expand their knowledge. A comprehensive review of the state-of-the-art Explainable AI methods starting from visualization, interpretable methods, local and global explanations, time series methods, and finishing with deep learning provides an unparalleled source of information currently unavailable anywhere else. Additionally, notebooks with vivid examples are a great supplement that makes the book even more attractive for practitioners of any level. Overall, the authors provide readers with an enormous breadth of coverage without losing sight of practical aspects, which makes this book truly unique and a great addition to the library of any data scientist. Dr. Andrey Sharapov, Product Data Scientist, Explainable AI Expert and Speaker, Founder of Explainable AI-XAI Group
Explainable Ai Foundations Methodologies And Applications
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Author : Mayuri Mehta
language : en
Publisher: Springer Nature
Release Date : 2022-10-19
Explainable Ai Foundations Methodologies And Applications written by Mayuri Mehta and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-10-19 with Technology & Engineering categories.
This book presents an overview and several applications of explainable artificial intelligence (XAI). It covers different aspects related to explainable artificial intelligence, such as the need to make the AI models interpretable, how black box machine/deep learning models can be understood using various XAI methods, different evaluation metrics for XAI, human-centered explainable AI, and applications of explainable AI in health care, security surveillance, transportation, among other areas. The book is suitable for students and academics aiming to build up their background on explainable AI and can guide them in making machine/deep learning models more transparent. The book can be used as a reference book for teaching a graduate course on artificial intelligence, applied machine learning, or neural networks. Researchers working in the area of AI can use this book to discover the recent developments in XAI. Besides its use in academia, this book could be used by practitioners in AI industries, healthcare industries, medicine, autonomous vehicles, and security surveillance, who would like to develop AI techniques and applications with explanations.
Explainable Deep Learning Ai
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Author : Jenny Benois-Pineau
language : en
Publisher: Elsevier
Release Date : 2023-02-20
Explainable Deep Learning Ai written by Jenny Benois-Pineau and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-02-20 with Computers categories.
Explainable Deep Learning AI: Methods and Challenges presents the latest works of leading researchers in the XAI area, offering an overview of the XAI area, along with several novel technical methods and applications that address explainability challenges for deep learning AI systems. The book overviews XAI and then covers a number of specific technical works and approaches for deep learning, ranging from general XAI methods to specific XAI applications, and finally, with user-oriented evaluation approaches. It also explores the main categories of explainable AI – deep learning, which become the necessary condition in various applications of artificial intelligence. The groups of methods such as back-propagation and perturbation-based methods are explained, and the application to various kinds of data classification are presented. - Provides an overview of main approaches to Explainable Artificial Intelligence (XAI) in the Deep Learning realm, including the most popular techniques and their use, concluding with challenges and exciting future directions of XAI - Explores the latest developments in general XAI methods for Deep Learning - Explains how XAI for Deep Learning is applied to various domains like images, medicine and natural language processing - Provides an overview of how XAI systems are tested and evaluated, specially with real users, a critical need in XAI
Explainable Ai Transparency And Accountability In Machine Learning
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Author : Mrs.J.Ramya
language : en
Publisher: SK Research Group of Companies
Release Date : 2025-09-18
Explainable Ai Transparency And Accountability In Machine Learning written by Mrs.J.Ramya and has been published by SK Research Group of Companies this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-09-18 with Computers categories.
Mrs.J.Ramya, Assistant Professor, Department of Computer Science and Applications, Agurchand Manmull Jain College, Chennai, Tamil Nadu, India. Dr.Kalpana.A, Assistant Professor, Department of Computer Applications, Agurchand Manmull Jain College, Chennai, Tamil Nadu, India.
Explainable Ai For Beginners
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Author : Amara Hawthorn
language : en
Publisher: Independently Published
Release Date : 2025-08-29
Explainable Ai For Beginners written by Amara Hawthorn and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-08-29 with Computers categories.
In an age where machine learning makes decisions about healthcare, finance, hiring, and justice, transparency matters more than ever. Explainable AI for Beginners is your friendly, step-by-step guide to building AI models that are not just accurate, but also clear, interpretable, and trustworthy. Written in plain English, this book cuts through the jargon to show you how to: Understand the fundamentals of Explainable AI (XAI) and why it's essential for fairness, safety, and accountability. Build simple, interpretable models using decision trees, linear models, and rule-based systems. Use practical XAI techniques-like SHAP values, LIME, and feature importance-to open the "black box" of complex models. Balance accuracy and interpretability so you can make informed trade-offs in real projects. Communicate insights clearly to non-technical stakeholders, regulators, and clients. Through relatable examples and hands-on exercises, you'll learn how to design AI systems that you-and others-can understand and trust. No advanced math or coding background required-just curiosity and the desire to build ethical, responsible AI. If you've ever wanted to peek inside the mind of an algorithm, or make machine learning less mysterious, this is the book for you.
Towards Explainable Fuzzy Ai Concepts Paradigms Tools And Techniques
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Author : Vladik Kreinovich
language : en
Publisher: Springer
Release Date : 2023-09-18
Towards Explainable Fuzzy Ai Concepts Paradigms Tools And Techniques written by Vladik Kreinovich and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-09-18 with Technology & Engineering categories.
Modern AI techniques –- especially deep learning –- provide, in many cases, very good recommendations: where a self-driving car should go, whether to give a company a loan, etc. The problem is that not all these recommendations are good -- and since deep learning provides no explanations, we cannot tell which recommendations are good. It is therefore desirable to provide natural-language explanation of the numerical AI recommendations. The need to connect natural language rules and numerical decisions is known since 1960s, when the need emerged to incorporate expert knowledge -- described by imprecise words like "small" -- into control and decision making. For this incorporation, a special "fuzzy" technique was invented, that led to many successful applications. This book described how this technique can help to make AI more explainable.The book can be recommended for students, researchers, and practitioners interested in explainable AI.
Explainable Ai For Beginners
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Author : Venugopala Ps
language : en
Publisher: Independently Published
Release Date : 2025-12-22
Explainable Ai For Beginners written by Venugopala Ps and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-12-22 with Computers categories.
Explainable AI (XAI) has rapidly emerged as one of the most essential areas in modern artificial intelligence, bridging the gap between powerful machine-learning models and the human need for clarity, trust, and accountability. As AI systems increasingly influence decisions in healthcare, finance, education, and everyday digital interactions, the ability to understand why a model behaves a certain way has become just as important as its accuracy. This book, Explainable AI for Beginners, is designed to offer a clear, structured, and beginner-friendly introduction to the concepts, methods, and practical tools that make AI interpretable. Whether you're a student stepping into the world of machine learning, a professional looking to demystify complex models, or an enthusiast curious about how AI "thinks," this book aims to be your accessible starting point. Each chapter builds from foundational ideas to hands-on techniques used in real-world applications. You will explore interpretable models, post-hoc explanation frameworks such as LIME and SHAP, and methods that bring transparency to deep learning systems. Beyond technical methods, this book emphasizes human-centered evaluation, ethical considerations, and future trends that are shaping the XAI landscape. The final capstone projects, including an explainable loan-approval assistant and an occlusion sensitivity experiment on synthetic MRI data, provide practical, end-to-end experience applying XAI principles. By the end, you will not only understand how to build explainable models but also why explainability is vital for creating accountable, trustworthy AI systems.
Knowledge Graphs For Explainable Artificial Intelligence Foundations Applications And Challenges
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Author : Freddy Lécué
language : en
Publisher: SAGE Publications Limited
Release Date : 2020-05-06
Knowledge Graphs For Explainable Artificial Intelligence Foundations Applications And Challenges written by Freddy Lécué and has been published by SAGE Publications Limited this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-05-06 with Computers categories.
The latest advances in Artificial Intelligence and (deep) Machine Learning in particular revealed a major drawback of modern intelligent systems, namely the inability to explain their decisions in a way that humans can easily understand. While eXplainable AI rapidly became an active area of research in response to this need for improved understandability and trustworthiness, the field of Knowledge Representation and Reasoning (KRR) has on the other hand a long-standing tradition in managing information in a symbolic, human-understandable form. This book provides the first comprehensive collection of research contributions on the role of knowledge graphs for eXplainable AI (KG4XAI), and the papers included here present academic and industrial research focused on the theory, methods and implementations of AI systems that use structured knowledge to generate reliable explanations. Introductory material on knowledge graphs is included for those readers with only a minimal background in the field, as well as specific chapters devoted to advanced methods, applications and case-studies that use knowledge graphs as a part of knowledge-based, explainable systems (KBX-systems). The final chapters explore current challenges and future research directions in the area of knowledge graphs for eXplainable AI. The book not only provides a scholarly, state-of-the-art overview of research in this subject area, but also fosters the hybrid combination of symbolic and subsymbolic AI methods, and will be of interest to all those working in the field.
Unveiling The Black Box
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Author : Sudipta Dey
language : en
Publisher: LAP Lambert Academic Publishing
Release Date : 2024-10-28
Unveiling The Black Box written by Sudipta Dey and has been published by LAP Lambert Academic Publishing this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-10-28 with Technology & Engineering categories.
Unveiling the Black Box: Practical Deep Learning and Explainable AI" offers a comprehensive overview of Explainable AI (XAI) techniques and their significance in ensuring transparency and trust in complex AI models. With AI applications spanning healthcare, finance, and autonomous systems, the opacity of deep learning models often raises ethical, legal, and reliability concerns. This guide explores foundational AI model structures, such as Feedforward Neural Networks (FNN), Convolutional Neural Networks (CNN), and Recurrent Neural Networks (RNN), highlighting their architecture, functionality, and real-world applications. To enhance interpretability, the text introduces leading XAI methods like Local Interpretable Model-Agnostic Explanations (LIME) and SHAPley Additive Explanations (SHAP), which enable users to understand model predictions. Advanced techniques, including Transfer Learning and Attention Mechanisms, are discussed to illustrate their impact on neural network adaptability and performance. The challenges of achieving interpretable AI, such as managing bias, balancing accuracy, and ensuring privacy, are also addressed.