Explainable Ai For Practitioners
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Explainable Ai For Practitioners
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Author : Michael Munn
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2022-10-31
Explainable Ai For Practitioners written by Michael Munn and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-10-31 with Computers categories.
Most intermediate-level machine learning books focus on how to optimize models by increasing accuracy or decreasing prediction error. But this approach often overlooks the importance of understanding why and how your ML model makes the predictions that it does. Explainability methods provide an essential toolkit for better understanding model behavior, and this practical guide brings together best-in-class techniques for model explainability. Experienced machine learning engineers and data scientists will learn hands-on how these techniques work so that you'll be able to apply these tools more easily in your daily workflow. This essential book provides: A detailed look at some of the most useful and commonly used explainability techniques, highlighting pros and cons to help you choose the best tool for your needs Tips and best practices for implementing these techniques A guide to interacting with explainability and how to avoid common pitfalls The knowledge you need to incorporate explainability in your ML workflow to help build more robust ML systems Advice about explainable AI techniques, including how to apply techniques to models that consume tabular, image, or text data Example implementation code in Python using well-known explainability libraries for models built in Keras and TensorFlow 2.0, PyTorch, and HuggingFace
Explainable Ai For Practitioners
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Author : Michael Munn
language : en
Publisher: O'Reilly Media
Release Date : 2023-01-31
Explainable Ai For Practitioners written by Michael Munn and has been published by O'Reilly Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-01-31 with categories.
Most intermediate-level Machine Learning books usually focus only on how to optimize models by increasing accuracy or decreasing prediction error. However, this focus often overlooks the importance and the need to be able to explain the "why" and "how" of why your ML model makes the predictions it does. This book brings together the best in class techniques for model interpretability and explaining model predictions in a hands-on approach so that experienced ML practitioners can more easily apply these tools in their daily workflow.
Explainable Artificial Intelligence An Introduction To Interpretable Machine Learning
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Author : Uday Kamath
language : en
Publisher: Springer
Release Date : 2021-12-16
Explainable Artificial Intelligence An Introduction To Interpretable Machine Learning written by Uday Kamath and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-12-16 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 For Healthcare
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Author : Aman Kataria
language : en
Publisher: CRC Press
Release Date : 2025-12-04
Explainable Ai For Healthcare written by Aman Kataria and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-12-04 with Computers categories.
This book explores the transformative potential of Explainable AI (XAI) in enhancing healthcare delivery and XAI's role in fostering transparency, trust, and accountability in AI-driven medical decision-making. Covering technical foundations, practical applications, and ethical considerations, it offers valuable insights into how XAI can improve clinical decision-making, patient outcomes, and healthcare operations. Through real-world case studies, the book illustrates the practical benefits of XAI in diverse healthcare scenarios. It also addresses the challenges and solutions related to deploying XAI, making it an essential resource for professionals and researchers. Detailed exploration of the methodologies, algorithms, and regulatory considerations underpinning XAI in smart healthcare systems Diverse case studies demonstrating practical applications and benefits of XAI across various healthcare domains, enhancing understanding through tangible examples Exploration of innovative XAI applications in diagnosis, treatment, patient monitoring, and care delivery, showcasing its potential to revolutionize healthcare practices and improve outcomes Discussion on how XAI promotes patient engagement by providing clear explanations of AI-driven diagnoses or treatment plans, enhancing patient understanding and participation in their healthcare Breakdown of XAI techniques, algorithms, and interpretability strategies, helping medical professionals understand and trust AI-driven decision-making processes
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 Ai In Health Informatics
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Author : Rajanikanth Aluvalu
language : en
Publisher: Springer Nature
Release Date : 2024-07-07
Explainable Ai In Health Informatics written by Rajanikanth Aluvalu and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-07-07 with Computers categories.
This book provides a comprehensive review of the latest research in the area of explainable artificial intelligence (XAI) in health informatics. It focuses on how explainable AI models can work together with humans to assist them in decision-making, leading to improved diagnosis and prognosis in healthcare. This book includes a collection of techniques and systems of XAI in health informatics and gives a wider perspective about the impact created by them. The book covers the different aspects, such as robotics, informatics, drugs, patients, etc., related to XAI in healthcare. The book is suitable for both beginners and advanced AI practitioners, including students, academicians, researchers, and industry professionals. It serves as an excellent reference for undergraduate and graduate-level courses on AI for medicine/healthcare or XAI for medicine/healthcare. Medical institutions can also utilize this book as reference material and provide tutorials to medical professionals on how the XAI techniques can contribute to trustworthy diagnosis and prediction of the diseases.
Introduction To Explainable Ai Xai
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Author : Robert Johnson
language : en
Publisher: HiTeX Press
Release Date : 2024-10-27
Introduction To Explainable Ai Xai written by Robert Johnson and has been published by HiTeX Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-10-27 with Computers categories.
"Introduction to Explainable AI (XAI): Making AI Understandable" is an essential resource for anyone seeking to understand the burgeoning field of explainable artificial intelligence. As AI systems become integral to critical decision-making processes across industries, the ability to interpret and comprehend their outputs becomes increasingly vital. This book offers a comprehensive exploration of XAI, delving into its foundational concepts, diverse techniques, and pivotal applications. It strives to demystify complex AI behaviors, ensuring that stakeholders across sectors can engage with AI technologies confidently and responsibly. Structured to cater to both beginners and those with an existing interest in AI, this book covers the spectrum of XAI topics, from model-specific approaches and interpretable machine learning to the ethical and societal implications of AI transparency. Readers will be equipped with practical insights into the tools and frameworks available for developing explainable models, alongside an understanding of the challenges and limitations inherent in the field. As we look toward the future, the book also addresses emerging trends and research directions, positioning itself as a definitive guide to navigating the evolving landscape of XAI. This book stands as an invaluable reference for students, practitioners, and policy makers alike, offering a balanced blend of theory and practical guidance. By focusing on the synergy between humans and machines through explainability, it underscores the importance of building AI systems that are not only powerful but also trustworthy and aligned with societal values.
Explainable Ai And User Experience Prototyping And Evaluating An Ux Optimized Xai Interface In Computer Vision
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Author : Georg Dedikov
language : en
Publisher: GRIN Verlag
Release Date : 2023-05-16
Explainable Ai And User Experience Prototyping And Evaluating An Ux Optimized Xai Interface In Computer Vision written by Georg Dedikov and has been published by GRIN Verlag this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-05-16 with Computers categories.
Master's Thesis from the year 2023 in the subject Computer Science - SEO, Search Engine Optimization, grade: 1,0, University of Regensburg (Professur für Wirtschaftsinformatik, insb. Internet Business & Digitale Soziale Medien), language: English, abstract: This thesis presents a toolkit of 17 user experience (UX) principles, which are categorized according to their relevance towards Explainable AI (XAI). The goal of Explainable AI has been widely associated in literature with dimensions of comprehensibility, usefulness, trust, and acceptance. Moreover, authors in academia postulate that research should rather focus on the development of holistic explanation interfaces instead of single visual explanations. Consequently, the focus of XAI research should be more on potential users and their needs, rather than purely technical aspects of XAI methods. Considering these three impediments, the author of this thesis derives the assumption to bring valuable insights from the research area of User Interface (UI) and User Experience design into XAI research. Basically, UX is concerned with the design and evaluation of pragmatic and hedonic aspects of a user’s interaction with a system in some context. These principles are taken into account in the subsequent prototyping of a custom XAI system called Brain Tumor Assistant (BTA). Here, a pre-trained EfficientNetB0 is used as a Convolutional Neural Network that can divide x-ray images of a human brain into four classes with an overall accuracy of 98%. To generate factual explanations, Local Interpretable Model-agnostic Explanations are subsequently applied as an XAI method. The following evaluation of the BTA is based on the so-called User Experience Questionnaire (UEQ) according to Laugwitz et al. (2008), whereby single items of the questionnaire are adapted to the specific context of XAI. Quantitative data from a study with 50 participants in each control and treatment group is used to present a standardized way of quantifying the dimensions of Usability and UX specifically for XAI systems. Furthermore, through an A/B test, evidence is presented that visual explanations have a significant (α=0.05) positive effect on the dimensions of attractiveness, usefulness, controllability, and trustworthiness. In summary, this thesis proves that explanations in computer vision not only have a significantly positive effect on trustworthiness, but also on other dimensions.
Biomedical Data Analysis And Processing Using Explainable Xai And Responsive Artificial Intelligence Rai
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Author : Aditya Khamparia
language : en
Publisher: Springer Nature
Release Date : 2022-04-09
Biomedical Data Analysis And Processing Using Explainable Xai And Responsive Artificial Intelligence Rai written by Aditya Khamparia and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-04-09 with Technology & Engineering categories.
The book discusses Explainable (XAI) and Responsive Artificial Intelligence (RAI) for biomedical and healthcare applications. It will discuss the advantages in dealing with big and complex data by using explainable AI concepts in the field of biomedical sciences. The book explains both positive as well as negative findings obtained by explainable AI techniques. It features real time experiences by physicians and medical staff for applied deep learning based solutions. The book will be extremely useful for researchers and practitioners in advancing their studies.
Explainable Ai In Healthcare
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Author : Mehul S Raval
language : en
Publisher: CRC Press
Release Date : 2023-07-17
Explainable Ai In Healthcare written by Mehul S Raval and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-07-17 with Medical categories.
This book combines technology and the medical domain. It covers advances in computer vision (CV) and machine learning (ML) that facilitate automation in diagnostics and therapeutic and preventive health care. The special focus on eXplainable Artificial Intelligence (XAI) uncovers the black box of ML and bridges the semantic gap between the technologists and the medical fraternity. Explainable AI in Healthcare: Unboxing Machine Learning for Biomedicine intends to be a premier reference for practitioners, researchers, and students at basic, intermediary levels and expert levels in computer science, electronics and communications, information technology, instrumentation and control, and electrical engineering. This book will benefit readers in the following ways: Explores state of art in computer vision and deep learning in tandem to develop autonomous or semi-autonomous algorithms for diagnosis in health care Investigates bridges between computer scientists and physicians being built with XAI Focuses on how data analysis provides the rationale to deal with the challenges of healthcare and making decision-making more transparent Initiates discussions on human-AI relationships in health care Unites learning for privacy preservation in health care