Explainable Human Ai Interaction
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Explainable Human Ai Interaction
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Author : Sarath Sreedharan
language : en
Publisher: Morgan & Claypool Publishers
Release Date : 2022-01-24
Explainable Human Ai Interaction written by Sarath Sreedharan and has been published by Morgan & Claypool Publishers this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-01-24 with Computers categories.
From its inception, artificial intelligence (AI) has had a rather ambivalent relationship with humans—swinging between their augmentation and replacement. Now, as AI technologies enter our everyday lives at an ever-increasing pace, there is a greater need for AI systems to work synergistically with humans. One critical requirement for such synergistic human‒AI interaction is that the AI systems' behavior be explainable to the humans in the loop. To do this effectively, AI agents need to go beyond planning with their own models of the world, and take into account the mental model of the human in the loop. At a minimum, AI agents need approximations of the human's task and goal models, as well as the human's model of the AI agent's task and goal models. The former will guide the agent to anticipate and manage the needs, desires and attention of the humans in the loop, and the latter allow it to act in ways that are interpretable to humans (by conforming to their mental models of it), and be ready to provide customized explanations when needed. The authors draw from several years of research in their lab to discuss how an AI agent can use these mental models to either conform to human expectations or change those expectations through explanatory communication. While the focus of the book is on cooperative scenarios, it also covers how the same mental models can be used for obfuscation and deception. The book also describes several real-world application systems for collaborative decision-making that are based on the framework and techniques developed here. Although primarily driven by the authors' own research in these areas, every chapter will provide ample connections to relevant research from the wider literature. The technical topics covered in the book are self-contained and are accessible to readers with a basic background in AI.
Explainable Human Ai Interaction
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Author : Sarath Sreedharan
language : en
Publisher: Springer Nature
Release Date : 2022-05-31
Explainable Human Ai Interaction written by Sarath Sreedharan and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-05-31 with Computers categories.
From its inception, artificial intelligence (AI) has had a rather ambivalent relationship with humans—swinging between their augmentation and replacement. Now, as AI technologies enter our everyday lives at an ever-increasing pace, there is a greater need for AI systems to work synergistically with humans. One critical requirement for such synergistic human‒AI interaction is that the AI systems' behavior be explainable to the humans in the loop. To do this effectively, AI agents need to go beyond planning with their own models of the world, and take into account the mental model of the human in the loop. At a minimum, AI agents need approximations of the human's task and goal models, as well as the human's model of the AI agent's task and goal models. The former will guide the agent to anticipate and manage the needs, desires and attention of the humans in the loop, and the latter allow it to act in ways that are interpretable to humans (by conforming to their mental models of it), andbe ready to provide customized explanations when needed. The authors draw from several years of research in their lab to discuss how an AI agent can use these mental models to either conform to human expectations or change those expectations through explanatory communication. While the focus of the book is on cooperative scenarios, it also covers how the same mental models can be used for obfuscation and deception. The book also describes several real-world application systems for collaborative decision-making that are based on the framework and techniques developed here. Although primarily driven by the authors' own research in these areas, every chapter will provide ample connections to relevant research from the wider literature. The technical topics covered in the book are self-contained and are accessible to readers with a basic background in AI.
Human Centered Ai
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Author : Ben Shneiderman
language : en
Publisher: Oxford University Press
Release Date : 2022
Human Centered Ai written by Ben Shneiderman and has been published by Oxford University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022 with Computers categories.
The remarkable progress in algorithms for machine and deep learning have opened the doors to new opportunities, and some dark possibilities. However, a bright future awaits those who build on their working methods by including HCAI strategies of design and testing. As many technology companies and thought leaders have argued, the goal is not to replace people, but to empower them by making design choices that give humans control over technology. In Human-Centered AI, Professor Ben Shneiderman offers an optimistic realist's guide to how artificial intelligence can be used to augment and enhance humans' lives. This project bridges the gap between ethical considerations and practical realities to offer a road map for successful, reliable systems. Digital cameras, communications services, and navigation apps are just the beginning. Shneiderman shows how future applications will support health and wellness, improve education, accelerate business, and connect people in reliable, safe, and trustworthy ways that respect human values, rights, justice, and dignity.
Explainable Artificial Intelligence
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Author : Luca Longo
language : en
Publisher: Springer Nature
Release Date : 2024-07-09
Explainable Artificial Intelligence written by Luca Longo and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-07-09 with Computers categories.
This four-volume set constitutes the refereed proceedings of the Second World Conference on Explainable Artificial Intelligence, xAI 2024, held in Valletta, Malta, during July 17-19, 2024. The 95 full papers presented were carefully reviewed and selected from 204 submissions. The conference papers are organized in topical sections on: Part I - intrinsically interpretable XAI and concept-based global explainability; generative explainable AI and verifiability; notion, metrics, evaluation and benchmarking for XAI. Part II - XAI for graphs and computer vision; logic, reasoning, and rule-based explainable AI; model-agnostic and statistical methods for eXplainable AI. Part III - counterfactual explanations and causality for eXplainable AI; fairness, trust, privacy, security, accountability and actionability in eXplainable AI. Part IV - explainable AI in healthcare and computational neuroscience; explainable AI for improved human-computer interaction and software engineering for explainability; applications of explainable artificial intelligence.
Explainable Agency In Artificial Intelligence
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Author : Silvia Tulli
language : en
Publisher: CRC Press
Release Date : 2024-01-22
Explainable Agency In Artificial Intelligence written by Silvia Tulli and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-01-22 with Computers categories.
This book focuses on a subtopic of explainable AI (XAI) called explainable agency (EA), which involves producing records of decisions made during an agent’s reasoning, summarizing its behavior in human-accessible terms, and providing answers to questions about specific choices and the reasons for them. We distinguish explainable agency from interpretable machine learning (IML), another branch of XAI that focuses on providing insight (typically, for an ML expert) concerning a learned model and its decisions. In contrast, explainable agency typically involves a broader set of AI-enabled techniques, systems, and stakeholders (e.g., end users), where the explanations provided by EA agents are best evaluated in the context of human subject studies. The chapters of this book explore the concept of endowing intelligent agents with explainable agency, which is crucial for agents to be trusted by humans in critical domains such as finance, self-driving vehicles, and military operations. This book presents the work of researchers from a variety of perspectives and describes challenges, recent research results, lessons learned from applications, and recommendations for future research directions in EA. The historical perspectives of explainable agency and the importance of interactivity in explainable systems are also discussed. Ultimately, this book aims to contribute to the successful partnership between humans and AI systems. Features: Contributes to the topic of explainable artificial intelligence (XAI) Focuses on the XAI subtopic of explainable agency Includes an introductory chapter, a survey, and five other original contributions
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 Artificial Intelligence For Autonomous Vehicles
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Author : Kamal Malik
language : en
Publisher: CRC Press
Release Date : 2024-08-14
Explainable Artificial Intelligence For Autonomous Vehicles written by Kamal Malik and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-08-14 with Computers categories.
Explainable 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.
Principles And Methods Of Explainable Artificial Intelligence In Healthcare
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Author : Victor Hugo C. De Albuquerque
language : en
Publisher: Medical Information Science Reference
Release Date : 2022
Principles And Methods Of Explainable Artificial Intelligence In Healthcare written by Victor Hugo C. De Albuquerque and has been published by Medical Information Science Reference this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022 with categories.
"This book focuses on the Explainable Artificial Intelligence (XAI) for healthcare, providing a broad overview of state-of-art approaches for accurate analysis and diagnosis, and encompassing computational vision processing techniques that handle complex data like physiological information, electronic healthcare records, medical imaging data that assist in earlier prediction"--
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.
Machine Learning For Health Informatics
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Author : Andreas Holzinger
language : en
Publisher: Springer
Release Date : 2016-12-09
Machine Learning For Health Informatics written by Andreas Holzinger and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-12-09 with Computers categories.
Machine learning (ML) is the fastest growing field in computer science, and Health Informatics (HI) is amongst the greatest application challenges, providing future benefits in improved medical diagnoses, disease analyses, and pharmaceutical development. However, successful ML for HI needs a concerted effort, fostering integrative research between experts ranging from diverse disciplines from data science to visualization. Tackling complex challenges needs both disciplinary excellence and cross-disciplinary networking without any boundaries. Following the HCI-KDD approach, in combining the best of two worlds, it is aimed to support human intelligence with machine intelligence. This state-of-the-art survey is an output of the international HCI-KDD expert network and features 22 carefully selected and peer-reviewed chapters on hot topics in machine learning for health informatics; they discuss open problems and future challenges in order to stimulate further research and international progress in this field.