Interpretable Ai
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Interpretable Ai
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Author : Ajay Thampi
language : en
Publisher: Simon and Schuster
Release Date : 2022-07-05
Interpretable Ai written by Ajay Thampi and has been published by Simon and Schuster this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-07-05 with Computers categories.
AI doesn't have to be a black box. These practical techniques help shine a light on your model's mysterious inner workings. Make your AI more transparent, and you'll improve trust in your results, combat data leakage and bias, and ensure compliance with legal requirements. Interpretable AI opens up the black box of your AI models. It teaches cutting-edge techniques and best practices that can make even complex AI systems interpretable. Each method is easy to implement with just Python and open source libraries. You'll learn to identify when you can utilize models that are inherently transparent, and how to mitigate opacity when your problem demands the power of a hard-to-interpret deep learning model.
Interpretable And Trustworthy Ai
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Author : Pethuru Raj
language : en
Publisher: CRC Press
Release Date : 2025-11-11
Interpretable And Trustworthy Ai written by Pethuru Raj and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-11-11 with Computers categories.
Users expect proper explanation and interpretability of all the decisions being taken by machine and deep learning (ML/ DL) algorithms. Interpretable and Trustworthy AI: Techniques and Frameworks covers key requirements for interpretability and trustworthiness of artificial intelligence (AI) models and how these needs can be met. This book explores artificial intelligence’s impact, limitations, and solutions. It examines AI’s role as a transformative technological paradigm. It explores how AI drives business advancement through intelligent software solutions, enabling automation, augmentation, and acceleration of IT-enabled business processes. The book establishes AI’s fundamental capacity to envision and implement sustainable business transformations. It addresses critical challenges in AI adoption, focusing on two key concerns: AI Interpretability: Models typically optimize for accuracy but struggle to capture real-world costs, especially regarding ethics and fairness. Interpretability features help understand model learning processes, available information, and decision justifications within real-world contexts. Trustworthy AI: Business leaders demand responsible AI solutions that prioritize human needs, safety, and privacy. Researchers are developing methods to enhance trust in AI models and their conclusions to accelerate adoption. Finally, the book presents techniques and approaches for creating sustainable, interpretable, and trustworthy AI models. It explores model-agnostic frameworks and methodologies designed to Trustworthy and Transparent AI, Explainable and Interpretable AI, Responsible AI, Generative AI, Agentic AI, and Efficient and Edge AI. With its comprehensive structure, the book provides a comprehensive examination of AI’s potential, its current limitations, and pathways to overcome these challenges for wider adoption.
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
Interpretable Artificial Intelligence A Perspective Of Granular Computing
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Author : Witold Pedrycz
language : en
Publisher: Springer Nature
Release Date : 2021-03-26
Interpretable Artificial Intelligence A Perspective Of Granular Computing written by Witold Pedrycz and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-03-26 with Technology & Engineering categories.
This book offers a comprehensive treatise on the recent pursuits of Artificial Intelligence (AI) – Explainable Artificial Intelligence (XAI) by casting the crucial features of interpretability and explainability in the original framework of Granular Computing. The innovative perspective established with the aid of information granules provides a high level of human centricity and transparency central to the development of AI constructs. The chapters reflect the breadth of the area and cover recent developments in the methodology, advanced algorithms and applications of XAI to visual analytics, knowledge representation, learning and interpretation. The book appeals to a broad audience including researchers and practitioners interested in gaining exposure to the rapidly growing body of knowledge in AI and intelligent systems.
Explainable Interpretable And Transparent Ai Systems
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Author : B. K. Tripathy
language : en
Publisher: CRC Press
Release Date : 2024-08-23
Explainable Interpretable And Transparent Ai Systems written by B. K. Tripathy and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-08-23 with Technology & Engineering categories.
Transparent Artificial Intelligence (AI) systems facilitate understanding of the decision-making process and provide opportunities in various aspects of explaining AI models. This book provides up-to-date information on the latest advancements in the field of explainable AI, which is a critical requirement of AI, Machine Learning (ML), and Deep Learning (DL) models. It provides examples, case studies, latest techniques, and applications from domains such as healthcare, finance, and network security. It also covers open-source interpretable tool kits so that practitioners can use them in their domains. Features: Presents a clear focus on the application of explainable AI systems while tackling important issues of “interpretability” and “transparency”. Reviews adept handling with respect to existing software and evaluation issues of interpretability. Provides insights into simple interpretable models such as decision trees, decision rules, and linear regression. Focuses on interpreting black box models like feature importance and accumulated local effects. Discusses capabilities of explainability and interpretability. This book is aimed at graduate students and professionals in computer engineering and networking communications.
Interpretable Artificial Intelligence A Perspective Of Granular Computing
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Author : Witold Pedrycz
language : en
Publisher:
Release Date : 2021
Interpretable Artificial Intelligence A Perspective Of Granular Computing written by Witold Pedrycz and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021 with categories.
This book offers a comprehensive treatise on the recent pursuits of Artificial Intelligence (AI) - Explainable Artificial Intelligence (XAI) by casting the crucial features of interpretability and explainability in the original framework of Granular Computing. The innovative perspective established with the aid of information granules provides a high level of human centricity and transparency central to the development of AI constructs. The chapters reflect the breadth of the area and cover recent developments in the methodology, advanced algorithms and applications of XAI to visual analytics, knowledge representation, learning and interpretation. The book appeals to a broad audience including researchers and practitioners interested in gaining exposure to the rapidly growing body of knowledge in AI and intelligent systems.
Interpretable Artificial Intelligence Using Nonlinear Decision Trees
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Author : Yashesh Deepakkumar Dhebar
language : en
Publisher:
Release Date : 2020
Interpretable Artificial Intelligence Using Nonlinear Decision Trees written by Yashesh Deepakkumar Dhebar and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020 with Electronic dissertations categories.
The recent times have observed a massive application of artificial intelligence (AI) to automate tasks across various domains. The back-end mechanism with which automation occurs is generally black-box. Some of the popular black-box AI methods used to solve an automation task include decision trees (DT), support vector machines (SVM), artificial neural networks (ANN), etc. In the past several years, these black-box AI methods have shown promising performance and have been widely applied and researched across industries and academia. While the black-box AI models have been shown to achieve high performance, the inherent mechanism with which a decision is made is hard to comprehend. This lack of interpretability and transparency of black-box AI methods makes them less trustworthy. In addition to this, the black-box AI models lack in their ability to provide valuable insights regarding the task at hand. Following these limitations of black-box AI models, a natural research direction of developing interpretable and explainable AI models has emerged and has gained an active attention in the machine learning and AI community in the past three years. In this dissertation, we will be focusing on interpretable AI solutions which are being currently developed at the Computational Optimization and Innovation Laboratory (COIN Lab) at Michigan State University. We propose a nonlinear decision tree (NLDT) based framework to produce transparent AI solutions for automation tasks related to classification and control. The recent advancement in non-linear optimization enables us to efficiently derive interpretable AI solutions for various automation tasks. The interpretable and transparent AI models induced using customized optimization techniques show similar or better performance as compared to complex black-box AI models across most of the benchmarks. The results are promising and provide directions to launch future studies in developing efficient transparent AI models.
Applied Computer Vision And Soft Computing With Interpretable Ai
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Author : Swati V. Shinde
language : en
Publisher: CRC Press
Release Date : 2023-10-05
Applied Computer Vision And Soft Computing With Interpretable Ai written by Swati V. Shinde and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-10-05 with Computers categories.
This reference text presents the knowledge base of computer vision and soft computing techniques with their applications for sustainable developments. Features: Covers a variety of deep learning architectures useful for computer vision tasks Demonstrates the use of different soft computing techniques and their applications for different computer vision tasks Highlights the unified strengths of hybrid techniques based on deep learning and soft computing taken together that give the interpretable, adaptive, and optimized solution to a given problem Addresses the different issues and further research opportunities in computer vision and soft computing Describes all the concepts with practical examples and case studies with appropriate performance measures that validate the applicability of the respective technique to a certain domain Considers recent real word problems and the prospective solutions to these problems This book will be useful to researchers, students, faculty, and industry personnel who are eager to explore the power of deep learning and soft computing for different computer vision tasks.
Explainable And Interpretable Models In Computer Vision And Machine Learning
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Author : Hugo Jair Escalante
language : en
Publisher: Springer
Release Date : 2018-11-29
Explainable And Interpretable Models In Computer Vision And Machine Learning written by Hugo Jair Escalante and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-11-29 with Computers categories.
This book compiles leading research on the development of explainable and interpretable machine learning methods in the context of computer vision and machine learning. Research progress in computer vision and pattern recognition has led to a variety of modeling techniques with almost human-like performance. Although these models have obtained astounding results, they are limited in their explainability and interpretability: what is the rationale behind the decision made? what in the model structure explains its functioning? Hence, while good performance is a critical required characteristic for learning machines, explainability and interpretability capabilities are needed to take learning machines to the next step to include them in decision support systems involving human supervision. This book, written by leading international researchers, addresses key topics of explainability and interpretability, including the following: · Evaluation and Generalization in Interpretable Machine Learning · Explanation Methods in Deep Learning · Learning Functional Causal Models with Generative Neural Networks · Learning Interpreatable Rules for Multi-Label Classification · Structuring Neural Networks for More Explainable Predictions · Generating Post Hoc Rationales of Deep Visual Classification Decisions · Ensembling Visual Explanations · Explainable Deep Driving by Visualizing Causal Attention · Interdisciplinary Perspective on Algorithmic Job Candidate Search · Multimodal Personality Trait Analysis for Explainable Modeling of Job Interview Decisions · Inherent Explainability Pattern Theory-based Video Event Interpretations