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Expert Witnessing and Scientific Testimony: A Guidebook, Second Edition
by Kenneth S. CohenBased on the author‘s more than 35 years of experience as a successful expert witness, this revised and expanded edition of Expert Witnessing and Scientific Testimony: A Guidebook demonstrates how to properly present scientific, criminal, and forensic testimony and survive the onslaught of cross-examination in court. It presents material in a step-
Experten-Forum Powertrain: Band 2: Simulation und Test (Proceedings)
by Alexander HeintzelHöhere elektrische Fahrzeug-Bordnetzspannungen eröffnen die Chance, bei Ladungswechsel, Verbrennung, Emissionierung und Reibung neue Lösungsansätze zu finden. Dies wird beim Experten-Forum Powertrain mit der ATZlive-Veranstaltung "Komponenten und Kompetenzen zukünftiger Antriebe" 2022 diskutiert. Die Tagung ist eine unverzichtbare Plattform für den Wissens- und Gedankenaustausch von Forschern und Entwicklern aller Unternehmen und Institutionen. Der Inhalt: Wasserstoff. - Thermomanagement. - Simulation elektrifizierter Antriebe. - Simulationsmethoden.
Experten-Forum Powertrain: Band 1: Elektrische Systemkomponenten und Speichertechnik (Proceedings)
by Alexander HeintzelHöhere elektrische Fahrzeug-Bordnetzspannungen eröffnen die Chance, bei Ladungswechsel, Verbrennung, Emissionierung und Reibung neue Lösungsansätze zu finden. Dies wird beim Experten-Forum Powertrain mit der ATZlive-Veranstaltung "Komponenten und Kompetenzen zukünftiger Antriebe" 2022 diskutiert. Die Tagung ist eine unverzichtbare Plattform für den Wissens- und Gedankenaustausch von Forschern und Entwicklern aller Unternehmen und Institutionen. Der Inhalt: Energiewandler. - Getriebe & Mechanik. - Energiespeicher. - Werkstoffe, Beschichtungen, Schmierstoffe.
Experten-Forum Powertrain: Tribologie im Wandel - Elektrifizierung, Materialoptimierung und Systemverständnis (Proceedings)
by Johannes LieblDie inhaltlichen Schwerpunkte des Tagungsbands zur ATZlive-Veranstaltung Experten-Forum Powertrain: Reibung in Antrieb und Fahrzeug 2019 liegen u.a. auf immer strengeren CO2-Grenzwerten und der Erfüllung der damit einhergehenden anspruchsvollen Prüfzyklen unter realen Fahrbedingungen. Die Tagung ist eine unverzichtbare Plattform für den Wissens- und Gedankenaustausch von Forschern und Entwicklern aller Unternehmen und Institutionen, die dieses Ziel verfolgen.
Experten-Forum Powertrain: Vom Prüfstand bis Big Data - ganzheitliche Validierung-in-the-Loop (Proceedings)
by Johannes LieblEin Schlüssel zu treffsicherer und effizienter Produktentwicklung liegt in der nahtlosen Verknüpfung von Simulation und Test in allen Phasen. Mit diesem umfassenden Ansatz wird die Elektrifizierung der Antriebe zum Treiber von Innovationen. Dies wird beim Experten-Forum Powertrain mit der ATZlive-Veranstaltung Simulation und Test 2019 diskutiert. Die Tagung ist eine unverzichtbare Plattform für den Wissens- und Gedankenaustausch von Forschern und Entwicklern aller Unternehmen und Institutionen.
Experten-Forum Powertrain: Moderne Antriebe – emissionsarm, elektrifiziert und variabel (Proceedings)
by Johannes LieblHöhere elektrische Fahrzeug-Bordnetzspannungen eröffnen die Chance, bei Ladungswechsel,Verbrennung, Emissionierung und Reibung neue Lösungsansätze zu finden. Dies wird beim Experten-Forum Powertrain mit der ATZlive-Veranstaltung Ladungswechsel und Emissionierung 2019 diskutiert.Die Tagung ist eine unverzichtbare Plattform für den Wissens- und Gedankenaustausch von Forschern und Entwicklern aller Unternehmen und Institutionen.
Expertise Out of Context: Proceedings of the Sixth International Conference on Naturalistic Decision Making (Expertise: Research And Applications Ser.)
by Robert R. HoffmanResearchers have revealed that real expertise, while applied to well-defined tasks with highly circumscribed contexts, often stretches beyond its routine boundaries. For example, a medical doctor may be called upon to diagnose a rare disease or perform emergency surgery outside his or her area of specialization because other experts are not availab
Expertise Under Scrutiny: 21st Century Decision Making for Environmental Health and Safety (Risk, Systems and Decisions)
by Myriam Merad Benjamin D. TrumpThis book explores the challenges that confront leaders in government and industry when making decisions in the areas of environmental health and safety. Today, decision making demands transparency, robustness, and resiliency. However thoughtfully they are devised, decisions made by governments and enterprises can often trigger immediate, passionate public response.Expertise Under Scrutiny shows how leaders can establish organizational decision making processes that yield valid, workable choices even in fast-changing and uncertain conditions.The first part of the book examines the organizational decision making process, describing the often-contentious environment in which important environmental health and safety decisions are made, and received. The authors review the roles of actors and experts in the decision making process. The book goes on to address such topics as:· The roles of actors and experts in the decision making process· Ethics and analytics as drivers of good decisions· Why managing problems in safety, security, environment, and health Part II offers an outline for adopting a formal decision support structure, including the use of decision support tools. It includes a chapter devoted to ELECTRE (ELimination and Choice Expressing Reality), a multi-criteria decision analysis system.The book concludes with an insightful appraisal and analysis of the expertise, structure and resources needed for navigating well-supported, risk-informed decisions in our 21st Century world.Expertise Under Scrutiny benefits a broad audience of students, academics, researchers, and working professionals in management and related disciplines, especially in the field of environmental health and safety.
Explainable AI and Other Applications of Fuzzy Techniques: Proceedings of the 2021 Annual Conference of the North American Fuzzy Information Processing Society, NAFIPS 2021 (Lecture Notes in Networks and Systems #258)
by Julia Rayz Victor Raskin Scott Dick Vladik KreinovichThis book focuses on an overview of the AI techniques, their foundations, their applications, and remaining challenges and open problems. Many artificial intelligence (AI) techniques do not explain their recommendations. Providing natural-language explanations for numerical AI recommendations is one of the main challenges of modern AI. To provide such explanations, a natural idea is to use techniques specifically designed to relate numerical recommendations and natural-language descriptions, namely fuzzy techniques. This book is of interest to practitioners who want to use fuzzy techniques to make AI applications explainable, to researchers who may want to extend the ideas from these papers to new application areas, and to graduate students who are interested in the state-of-the-art of fuzzy techniques and of explainable AI—in short, to anyone who is interested in problems involving fuzziness and AI in general.
Explainable AI for Cybersecurity
by Zhixin Pan Prabhat MishraThis book provides a comprehensive overview of security vulnerabilities and state-of-the-art countermeasures using explainable artificial intelligence (AI). Specifically, it describes how explainable AI can be effectively used for detection and mitigation of hardware vulnerabilities (e.g., hardware Trojans) as well as software attacks (e.g., malware and ransomware). It provides insights into the security threats towards machine learning models and presents effective countermeasures. It also explores hardware acceleration of explainable AI algorithms. The reader will be able to comprehend a complete picture of cybersecurity challenges and how to detect them using explainable AI. This book serves as a single source of reference for students, researchers, engineers, and practitioners for designing secure and trustworthy systems.
Explainable AI for Education: Recent Trends and Challenges (Information Systems Engineering and Management #19)
by Tanu Singh Soumi Dutta Sonali Vyas Álvaro Rocha“Explainable AI for Education: Recent Trends and Challenges” is a comprehensive exploration of the intersection between artificial intelligence (AI) and education. In this book, we delve into the critical need for transparency and interpretability in AI systems deployed within educational contexts. Key Themes Understanding AI in Education: We provide a concise overview of AI techniques commonly used in educational settings, including recommendation systems, personalized learning, and assessment tools. Readers will gain insights into the potential benefits and risks associated with AI adoption in education. The Black-Box Problem: AI models often operate as “black boxes,” making it challenging to understand their decision-making processes. We discuss the implications of this opacity and emphasize the importance of explainability. Explainable AI (XAI) Techniques: From rule-based approaches to neural network interpretability, we explore various methods for making AI models more transparent. Examples and case studies illustrate how XAI can enhance educational outcomes. Ethical Considerations: As AI becomes more integrated into education, ethical dilemmas arise. We address issues related to bias, fairness, and accountability, emphasizing responsible AI practices. Future Directions: Our book looks ahead, considering the evolving landscape of AI and its impact on education. We propose research directions and practical steps to promote XAI adoption in educational institutions.
Explainable AI: Foundations, Methodologies and Applications (Intelligent Systems Reference Library #232)
by Mayuri Mehta Vasile Palade Indranath ChatterjeeThis 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 (Computational Intelligence Methods and Applications)
by Patrick Siarry Mayuri Mehta Rajanikanth AluvaluThis 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.
Explainable AI in Healthcare and Medicine: Building a Culture of Transparency and Accountability (Studies in Computational Intelligence #914)
by Arash Shaban-Nejad Martin Michalowski David L. BuckeridgeThis book highlights the latest advances in the application of artificial intelligence and data science in health care and medicine. Featuring selected papers from the 2020 Health Intelligence Workshop, held as part of the Association for the Advancement of Artificial Intelligence (AAAI) Annual Conference, it offers an overview of the issues, challenges, and opportunities in the field, along with the latest research findings. Discussing a wide range of practical applications, it makes the emerging topics of digital health and explainable AI in health care and medicine accessible to a broad readership. The availability of explainable and interpretable models is a first step toward building a culture of transparency and accountability in health care. As such, this book provides information for scientists, researchers, students, industry professionals, public health agencies, and NGOs interested in the theory and practice of computational models of public and personalized health intelligence.
Explainable AI Within the Digital Transformation and Cyber Physical Systems: XAI Methods and Applications
by Moamar Sayed-MouchawehThis book presents Explainable Artificial Intelligence (XAI), which aims at producing explainable models that enable human users to understand and appropriately trust the obtained results. The authors discuss the challenges involved in making machine learning-based AI explainable. Firstly, that the explanations must be adapted to different stakeholders (end-users, policy makers, industries, utilities etc.) with different levels of technical knowledge (managers, engineers, technicians, etc.) in different application domains. Secondly, that it is important to develop an evaluation framework and standards in order to measure the effectiveness of the provided explanations at the human and the technical levels. This book gathers research contributions aiming at the development and/or the use of XAI techniques in order to address the aforementioned challenges in different applications such as healthcare, finance, cybersecurity, and document summarization. It allows highlighting the benefits and requirements of using explainable models in different application domains in order to provide guidance to readers to select the most adapted models to their specified problem and conditions.Includes recent developments of the use of Explainable Artificial Intelligence (XAI) in order to address the challenges of digital transition and cyber-physical systems;Provides a textual scientific description of the use of XAI in order to address the challenges of digital transition and cyber-physical systems;Presents examples and case studies in order to increase transparency and understanding of the methodological concepts.
Explainable AI (XAI) for Sustainable Development: Trends and Applications
by Seifedine Kadry Rajesh Kumar Dhanaraj Ravi Shekhar Tiwari Lakshmi D.This book presents innovative research works to automate, innovate, design, and deploy AI fo real-world applications. It discusses AI applications in major cutting-edge technologies and details about deployment solutions for different applications for sustainable development. The application of Blockchain techniques illustrates the ways of optimisation algorithms in this book. The challenges associated with AI deployment are also discussed in detail, and edge computing with machine learning solutions is explained. This book provides multi-domain applications of AI to the readers to help find innovative methods towards the business, sustainability, and customer outreach paradigms in the AI domain.• Focuses on virtual machine placement and migration techniques for cloud data centres• Presents the role of machine learning and meta-heuristic approaches for optimisation in cloud computing services• Includes application of placement techniques for quality of service, performance, and reliability improvement• Explores data centre resource management, load balancing and orchestration using machine learning techniques• Analyses dynamic and scalable resource scheduling with a focus on resource managementThe reference work is for postgraduate students, professionals, and academic researchers in computer science and information technology.
Explainable Ambient Intelligence: Explainable Artificial Intelligence Applications in Smart Life (SpringerBriefs in Applied Sciences and Technology)
by Tin-Chih Toly ChenThis book systematically reviews the progress of Explainable Ambient Intelligence (XAmI) and introduces its methods, tools, and applications. Ambient intelligence (AmI) is a vision in which an environment supports the people inhabiting it in an unobtrusive, interconnected, adaptable, dynamic, embedded, and intelligent way. So far, artificial intelligence (AI) technologies have been widely applied in AmI. However, some advanced AI methods are not easy to understand or communicate, especially for users with insufficient background knowledge of AI, which undoubtedly limits the practicability of these methods. To address this issue, explainable AI (XAI) has been considered a viable strategy. Although XAI technologies and tools applied in other fields can also be applied to explain AI technology applications in AmI, users should be the main body in the application of AmI, which is slightly different from the application of AI technologies in other fields. This book containsreal case studies of the application of XAml and is a valuable resource for students and researchers.
Explainable and Interpretable Reinforcement Learning for Robotics (Synthesis Lectures on Artificial Intelligence and Machine Learning)
by Aaron M. Roth Dinesh Manocha Ram D. Sriram Elham TabassiThis book surveys the state of the art in explainable and interpretable reinforcement learning (RL) as relevant for robotics. While RL in general has grown in popularity and been applied to increasingly complex problems, several challenges have impeded the real-world adoption of RL algorithms for robotics and related areas. These include difficulties in preventing safety constraints from being violated and the issues faced by systems operators who desire explainable policies and actions. Robotics applications present a unique set of considerations and result in a number of opportunities related to their physical, real-world sensory input and interactions. The authors consider classification techniques used in past surveys and papers and attempt to unify terminology across the field. The book provides an in-depth exploration of 12 attributes that can be used to classify explainable/interpretable techniques. These include whether the RL method is model-agnostic or model-specific, self-explainable or post-hoc, as well as additional analysis of the attributes of scope, when-produced, format, knowledge limits, explanation accuracy, audience, predictability, legibility, readability, and reactivity. The book is organized around a discussion of these methods broken down into 42 categories and subcategories, where each category can be classified according to some of the attributes. The authors close by identifying gaps in the current research and highlighting areas for future investigation.
Explainable Artificial Intelligence: Methodology, Tools, and Applications (SpringerBriefs in Applied Sciences and Technology)
by Tin-Chih Toly ChenThis book provides a comprehensive overview of the latest developments in Explainable AI (XAI) and its applications in manufacturing. It covers the various methods, tools, and technologies that are being used to make AI more understandable and communicable for factory workers. With the increasing use of AI in manufacturing, there is a growing need to address the limitations of advanced AI methods that are difficult to understand or explain to those without a background in AI. This book addresses this need by providing a systematic review of the latest research and advancements in XAI specifically tailored for the manufacturing industry.The book includes real-world case studies and examples to illustrate the practical applications of XAI in manufacturing. It is a valuable resource for researchers, engineers, and practitioners working in the field of AI and manufacturing.
Explainable Artificial Intelligence Based on Neuro-Fuzzy Modeling with Applications in Finance (Studies in Computational Intelligence #964)
by Tom RutkowskiThe book proposes techniques, with an emphasis on the financial sector, which will make recommendation systems both accurate and explainable. The vast majority of AI models work like black box models. However, in many applications, e.g., medical diagnosis or venture capital investment recommendations, it is essential to explain the rationale behind AI systems decisions or recommendations. Therefore, the development of artificial intelligence cannot ignore the need for interpretable, transparent, and explainable models. First, the main idea of the explainable recommenders is outlined within the background of neuro-fuzzy systems. In turn, various novel recommenders are proposed, each characterized by achieving high accuracy with a reasonable number of interpretable fuzzy rules. The main part of the book is devoted to a very challenging problem of stock market recommendations. An original concept of the explainable recommender, based on patterns from previous transactions, is developed; it recommends stocks that fit the strategy of investors, and its recommendations are explainable for investment advisers.
Explainable Artificial Intelligence for Autonomous Vehicles: Concepts, Challenges, and Applications (Explainable AI (XAI) for Engineering Applications)
by Kamal Malik Moolchand Sharma Suman Deswal Umesh Gupta Deevyankar Agarwal Yahya Obaid Bakheet Al ShamsiExplainable AI for Autonomous Vehicles: Concepts, Challenges, and Applications is a comprehensive guide to developing and applying explainable artificial intelligence (XAI) in the context of autonomous vehicles. It begins with an introduction to XAI and its importance in developing autonomous vehicles. It also provides an overview of the challenges and limitations of traditional black-box AI models and how XAI can help address these challenges by providing transparency and interpretability in the decision-making process of autonomous vehicles. The book then covers the state-of-the-art techniques and methods for XAI in autonomous vehicles, including model-agnostic approaches, post-hoc explanations, and local and global interpretability techniques. It also discusses the challenges and applications of XAI in autonomous vehicles, such as enhancing safety and reliability, improving user trust and acceptance, and enhancing overall system performance. Ethical and social considerations are also addressed in the book, such as the impact of XAI on user privacy and autonomy and the potential for bias and discrimination in XAI-based systems. Furthermore, the book provides insights into future directions and emerging trends in XAI for autonomous vehicles, such as integrating XAI with other advanced technologies like machine learning and blockchain and the potential for XAI to enable new applications and services in the autonomous vehicle industry. Overall, the book aims to provide a comprehensive understanding of XAI and its applications in autonomous vehicles to help readers develop effective XAI solutions that can enhance autonomous vehicle systems' safety, reliability, and performance while improving user trust and acceptance.This book: Discusses authentication mechanisms for camera access, encryption protocols for data protection, and access control measures for camera systems. Showcases challenges such as integration with existing systems, privacy, and security concerns while implementing explainable artificial intelligence in autonomous vehicles. Covers explainable artificial intelligence for resource management, optimization, adaptive control, and decision-making. Explains important topics such as vehicle-to-vehicle (V2V) communication, vehicle-to-infrastructure (V2I) communication, remote monitoring, and control. Emphasizes enhancing safety, reliability, overall system performance, and improving user trust in autonomous vehicles. The book is intended to provide researchers, engineers, and practitioners with a comprehensive understanding of XAI's key concepts, challenges, and applications in the context of autonomous vehicles. It is primarily written for senior undergraduate, graduate students, and academic researchers in the fields of electrical engineering, electronics and communication engineering, computer science and engineering, information technology, and automotive engineering.
Explainable Artificial Intelligence for Biomedical and Healthcare Applications (Explainable AI (XAI) for Engineering Applications)
by Aditya Khamparia and Deepak GuptaThis reference text helps us understand how the concepts of explainable artificial intelligence (XAI) are used in the medical and healthcare sectors. The text discusses medical robotic systems using XAI and physical devices having autonomous behaviors for medical operations. It explores the usage of XAI for analyzing different types of unique data sets for medical image analysis, medical image registration, medical data synthesis, and information discovery. It covers important topics including XAI for biometric security, genomics, and medical disease diagnosis.This book:• Provides an excellent foundation for the core concepts and principles of explainable AI in biomedical and healthcare applications.• Covers explainable AI for robotics and autonomous systems.• Discusses usage of explainable AI in medical image analysis, medical image registration, and medical data synthesis.• Examines biometrics security-assisted applications and their integration using explainable AI.The text will be useful for graduate students, professionals, and academic researchers in diverse areas such as electrical engineering, electronics and communication engineering, biomedical engineering, and computer science.
Explainable Artificial Intelligence for Biomedical Applications (River Publishers Series in Biomedical Engineering)
by Utku Kose Deepak Gupta Xi ChenSince its first appearance, artificial intelligence has been ensuring revolutionary outcomes in the context of real-world problems. At this point, it has strong relations with biomedical and today’s intelligent systems compete with human capabilities in medical tasks. However, advanced use of artificial intelligence causes intelligent systems to be black-box. That situation is not good for building trustworthy intelligent systems in medical applications. For a remarkable amount of time, researchers have tried to solve the black-box issue by using modular additions, which have led to the rise of the term: interpretable artificial intelligence. As the literature matured (as a result of, in particular, deep learning), that term transformed into explainable artificial intelligence (XAI). This book provides an essential edited work regarding the latest advancements in explainable artificial intelligence (XAI) for biomedical applications. It includes not only introductive perspectives but also applied touches and discussions regarding critical problems as well as future insights. Topics discussed in the book include: XAI for the applications with medical images XAI use cases for alternative medical data/task Different XAI methods for biomedical applications Reviews for the XAI research for critical biomedical problems. Explainable Artificial Intelligence for Biomedical Applications is ideal for academicians, researchers, students, engineers, and experts from the fields of computer science, biomedical, medical, and health sciences. It also welcomes all readers of different fields to be informed about use cases of XAI in black-box artificial intelligence. In this sense, the book can be used for both teaching and reference source purposes.
Explainable Artificial Intelligence for Cyber Security: Next Generation Artificial Intelligence (Studies in Computational Intelligence #1025)
by Mohiuddin Ahmed Sheikh Rabiul Islam Adnan Anwar Nour Moustafa Al-Sakib Khan PathanThis book presents that explainable artificial intelligence (XAI) is going to replace the traditional artificial, machine learning, deep learning algorithms which work as a black box as of today. To understand the algorithms better and interpret the complex networks of these algorithms, XAI plays a vital role. In last few decades, we have embraced AI in our daily life to solve a plethora of problems, one of the notable problems is cyber security. In coming years, the traditional AI algorithms are not able to address the zero-day cyber attacks, and hence, to capitalize on the AI algorithms, it is absolutely important to focus more on XAI. Hence, this book serves as an excellent reference for those who are working in cyber security and artificial intelligence.
Explainable Artificial Intelligence for Intelligent Transportation Systems
by Amina Adadi Afaf BouhouteArtificial Intelligence (AI) and Machine Learning (ML) are set to revolutionize all industries, and the Intelligent Transportation Systems (ITS) field is no exception. While ML, especially deep learning models, achieve great performance in terms of accuracy, the outcomes provided are not amenable to human scrutiny and can hardly be explained. This can be very problematic, especially for systems of a safety-critical nature such as transportation systems. Explainable AI (XAI) methods have been proposed to tackle this issue by producing human interpretable representations of machine learning models while maintaining performance. These methods hold the potential to increase public acceptance and trust in AI-based ITS. FEATURES: Provides the necessary background for newcomers to the field (both academics and interested practitioners) Presents a timely snapshot of explainable and interpretable models in ITS applications Discusses ethical, societal, and legal implications of adopting XAI in the context of ITS Identifies future research directions and open problems