Xxai Beyond Explainable Ai
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| Author | : Andreas Holzinger |
| Publisher | : Springer Nature |
| Total Pages | : 397 |
| Release | : 2022 |
| Genre | : Artificial intelligence |
| ISBN | : 303104083X |
This is an open access book. Statistical machine learning (ML) has triggered a renaissance of artificial intelligence (AI). While the most successful ML models, including Deep Neural Networks (DNN), have developed better predictivity, they have become increasingly complex, at the expense of human interpretability (correlation vs. causality). The field of explainable AI (xAI) has emerged with the goal of creating tools and models that are both predictive and interpretable and understandable for humans. Explainable AI is receiving huge interest in the machine learning and AI research communities, across academia, industry, and government, and there is now an excellent opportunity to push towards successful explainable AI applications. This volume will help the research community to accelerate this process, to promote a more systematic use of explainable AI to improve models in diverse applications, and ultimately to better understand how current explainable AI methods need to be improved and what kind of theory of explainable AI is needed. After overviews of current methods and challenges, the editors include chapters that describe new developments in explainable AI. The contributions are from leading researchers in the field, drawn from both academia and industry, and many of the chapters take a clear interdisciplinary approach to problem-solving. The concepts discussed include explainability, causability, and AI interfaces with humans, and the applications include image processing, natural language, law, fairness, and climate science.
| Author | : Utku Kose |
| Publisher | : CRC Press |
| Total Pages | : 251 |
| Release | : 2024-04-23 |
| Genre | : Medical |
| ISBN | : 1040020453 |
This book highlights the use of explainable artificial intelligence (XAI) for healthcare problems, in order to improve trustworthiness, performance and sustainability levels in the context of applications. Explainable Artificial Intelligence (XAI) in Healthcare adopts the understanding that AI solutions should not only have high accuracy performance, but also be transparent, understandable and reliable from the end user's perspective. The book discusses the techniques, frameworks, and tools to effectively implement XAI methodologies in critical problems of healthcare field. The authors offer different types of solutions, evaluation methods and metrics for XAI and reveal how the concept of explainability finds a response in target problem coverage. The authors examine the use of XAI in disease diagnosis, medical imaging, health tourism, precision medicine and even drug discovery. They also point out the importance of user perspectives and value of the data used in target problems. Finally, the authors also ensure a well-defined future perspective for advancing XAI in terms of healthcare. This book will offer great benefits to students at the undergraduate and graduate levels and researchers. The book will also be useful for industry professionals and clinicians who perform critical decision-making tasks.
| Author | : Luca Longo |
| Publisher | : Springer Nature |
| Total Pages | : 508 |
| Release | : |
| Genre | : |
| ISBN | : 3031637879 |
| Author | : Zhixin Pan |
| Publisher | : Springer Nature |
| Total Pages | : 249 |
| Release | : 2024-01-13 |
| Genre | : Technology & Engineering |
| ISBN | : 3031464796 |
This 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.
| Author | : Ghonge, Mangesh M. |
| Publisher | : IGI Global |
| Total Pages | : 523 |
| Release | : 2024-01-18 |
| Genre | : Computers |
| ISBN | : 1668463636 |
As smart cities become more prevalent, the need for explainable AI (XAI) applications has become increasingly important. Advances in Explainable AI Applications for Smart Cities is a co-edited book that showcases the latest research and development in XAI for smart city applications. This book covers a wide range of topics, including medical diagnosis, finance and banking, judicial systems, military training, manufacturing industries, autonomous vehicles, insurance claim management, and cybersecurity solutions. Through its diverse case studies and research, this book provides valuable insights into the importance of XAI in smart city applications. This book is an essential resource for undergraduate and postgraduate students, researchers, academicians, industry professionals, and scientists working in research laboratories. It provides a comprehensive overview of XAI concepts, advantages over AI, and its applications in smart city development. By showcasing the impact of XAI on various smart city applications, the book enables readers to understand the importance of XAI in creating more sustainable and efficient smart cities. Additionally, the book addresses the open challenges and research issues related to XAI in modern smart cities, providing a roadmap for future research in this field. Overall, this book is a valuable resource for anyone interested in understanding the importance of XAI in smart city applications.
| Author | : Jenny Benois-Pineau |
| Publisher | : Elsevier |
| Total Pages | : 348 |
| Release | : 2023-02-20 |
| Genre | : Computers |
| ISBN | : 0323993885 |
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
| Author | : Albert Sanchez-Graells |
| Publisher | : Oxford University Press |
| Total Pages | : 488 |
| Release | : 2024-01-10 |
| Genre | : Law |
| ISBN | : 019263660X |
The digital transformation of the public sector has accelerated. States are experimenting with technology, seeking more streamlined and efficient digital government and public services. However, there are significant concerns about the risks and harms to individual and collective rights under new modes of digital public governance. Several jurisdictions are attempting to regulate digital technologies, especially artificial intelligence, however regulatory effort primarily concentrates on technology use by companies, not by governments. The regulatory gap underpinning public sector digitalisation is growing. As it controls the acquisition of digital technologies, public procurement has emerged as a 'regulatory fix' to govern public sector digitalisation. It seeks to ensure through its contracts that public sector digitalisation is trustworthy, ethical, responsible, transparent, fair, and (cyber) safe. However, in Digital Technologies and Public Procurement: Gatekeeping and Experimentation in Digital Public Governance, Albert Sanchez-Graells argues that procurement cannot perform this gatekeeping role effectively. Through a detailed case study of procurement digitalisation as a site of unregulated technological experimentation, he demonstrates that relying on 'regulation by contract' creates a false sense of security in governing the transition towards digital public governance. This leaves the public sector exposed to the 'policy irresistibility' that surrounds hyped digital technologies. Bringing together insights from political economy, public policy, science, technology, and legal scholarship, this thought-provoking book proposes an alternative regulatory approach and contributes to broader debates of digital constitutionalism and digital technology regulation.
| Author | : Song Gao |
| Publisher | : CRC Press |
| Total Pages | : 469 |
| Release | : 2023-12-29 |
| Genre | : Technology & Engineering |
| ISBN | : 1003814921 |
This comprehensive handbook covers Geospatial Artificial Intelligence (GeoAI), which is the integration of geospatial studies and AI machine (deep) learning and knowledge graph technologies. It explains key fundamental concepts, methods, models, and technologies of GeoAI, and discusses the recent advances, research tools, and applications that range from environmental observation and social sensing to natural disaster responses. As the first single volume on this fast-emerging domain, Handbook of Geospatial Artificial Intelligence is an excellent resource for educators, students, researchers, and practitioners utilizing GeoAI in fields such as information science, environment and natural resources, geosciences, and geography. Features Provides systematic introductions and discussions of GeoAI theory, methods, technologies, applications, and future perspectives Covers a wide range of GeoAI applications and case studies in practice Offers supplementary materials such as data, programming code, tools, and case studies Discusses the recent developments of GeoAI methods and tools Includes contributions written by top experts in cutting-edge GeoAI topics This book is intended for upper-level undergraduate and graduate students from different disciplines and those taking GIS courses in geography or computer sciences as well as software engineers, geospatial industry engineers, GIS professionals in non-governmental organizations, and federal/state agencies who use GIS and want to learn more about GeoAI advances and applications.
| Author | : Anna Visvizi |
| Publisher | : Springer Nature |
| Total Pages | : 798 |
| Release | : 2024-01-01 |
| Genre | : Computers |
| ISBN | : 3031447212 |
This book features research presented and discussed during the Research & Innovation Forum (Rii Forum) 2023. As such, this book offers a unique insight into emerging topics, issues and developments pertinent to the fields of technology, innovation and education and their social impact. Papers included in this book apply inter- and multi-disciplinary approaches to query such issues as technology-enhanced teaching and learning, smart cities, information systems, cognitive computing and social networking. What brings these threads of the discussion together is the question of how advances in computer science—which are otherwise largely incomprehensible to researchers from other fields—can be effectively translated and capitalized on so as to make them beneficial for society as a whole. In this context, Rii Forum and Rii Forum proceedings offer an essential venue where diverse stakeholders, including academics, the think tank sector and decision-makers, can engage in a meaningful dialogue with a view to improving the applicability of advances in computer science.
| Author | : Lukas Böffel |
| Publisher | : Springer Nature |
| Total Pages | : 439 |
| Release | : 2022-12-30 |
| Genre | : Business & Economics |
| ISBN | : 3031170776 |
This book covers three topics that have dominated financial market regulation and supervision debates: digital finance, sustainable finance, and the Banking and Capital Markets Union. Within the first part, seven chapters will tackle specific questions arising in digital finance, including but not limited to artificial intelligence, tokenisation, and international regulatory cooperation in digital financial services. The second part addresses one of humanity’s most pressing issues today: the climate crisis. The quest for sustainable finance is driven by political actors and a common understanding that climate change is a severe threat. As financial institutions are a cornerstone of human interaction, they are in the regulatory spotlight. The chapters explore sustainability in EU banking and insurance regulation, the interrelationship between systemic risk and sustainability, and the ‘greening’ of EU monetary policy. The third part analyses two projects that have led to huge structural changes in the European financial market architecture over the last decade: the European Banking Union and Capital Markets Union. This transformation has raised numerous legal questions that can only gradually be answered in all their intricacies. In four chapters, this book examines composite procedures, property rights of depositors in banking resolution, preemptive financing arrangements and the phenomenon of subsidiarisation in the context of Brexit. Of interest to academics, policymakers, practitioners, and students in the field of EU financial regulation, banking law, securities law, and regulatory law, this book offers a compilation of analyses on pressing banking and capital markets law problems.
