{"product_id":"advances-in-human-ai-collaboration-hardback-9781394266371","title":"Advances in Human-AI Collaboration (Hardback) 9781394266371","description":"\u003cfont face=\"Georgia\"\u003e\r\n\u003cp\u003e\u003cfont size=\"6\"\u003eAdvances in Human-AI Collaboration\u003c\/font\u003e\u003cbr\u003e\r\n\r\n\r\n\r\n\r\n\r\n\u003c\/p\u003e\n\u003cp\u003e\u003cfont size=\"4\"\u003eVincent G. Duffy (Edited by), Duffy (Author), Waldemar Karwowski (Edited by), Gavriel Salvendy (Edited by)\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e9781394266371, Wiley\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eHardback, published 14 April 2026\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e368 pages\u003cbr\u003e24.4 x 19.6 x 2.8 cm, 0.862 kg\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\r\n\r\n\u003cp align=\"justify\"\u003e\u003cstrong\u003e\u003cfont size=\"3\"\u003e\u003cp\u003e\u003cb\u003eDetailed guide on how humans and AI systems work in tandem, focused on the successful deployment and use of applications\u003c\/b\u003e \u003c\/p\u003e\n\u003cp\u003e\u003ci\u003eAdvances in Human-AI Collaboration\u003c\/i\u003e offers a comprehensive exploration of AI technologies and applications in the field of Industrial and Systems Engineering. The book incorporates knowledge about AI technology, methodologies, and tools for enabling human-AI collaboration at work, covers the effective design and use of systems and operations utilizing human-AI collaboration to benefit productivity, quality, and customer satisfaction, and provides readers with the skills necessary to effectively implement and consider human-AI collaboration across a variety of settings. \u003c\/p\u003e\n\u003cp\u003eThis book delivers insights on a wide range of topics including similarities and differences of human and artificial intelligence, effort in creating algorithms versus meeting user needs and enabling improved decision support, sentiment analysis and language models, AI tutors and their design, engagement, and theory building, situation awareness of AI models in relation to human performance, and fact-checking beyond machine learning and predictive accuracy. \u003c\/p\u003e\n\u003cp\u003eWritten by a team of highly qualified academics with significant experience in the field, \u003ci\u003eAdvances in Human-AI Collaboration\u003c\/i\u003e includes information on: \u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eAutonomous vehicles and delivery systems, covering sensors and perception as well as adoption rate and safety projections\u003c\/li\u003e\n\u003cli\u003eChat-based customer service, covering theory-based interventions to enhance public services and examples of intentional human-technology interaction\u003c\/li\u003e\n\u003cli\u003eShip safety, covering increased automation and machine vision to enable collision avoidance\u003c\/li\u003e\n\u003cli\u003e Strategies for moving beyond passive writing assistance and writing-related best practices\u003c\/li\u003e\n\u003cli\u003eVulnerabilities in technology-centered design including biased and distorted data, with examples of real-world accidents\u003c\/li\u003e\n\u003c\/ul\u003e \u003cp\u003e\u003ci\u003eAdvances in Human-AI Collaboration\u003c\/i\u003e is an essential read for industry practitioners and corporate researchers concerned with using AI in integrated system design and operation. The book also provides essential knowledge for academics researching AI and integrated systems.\u003c\/p\u003e\u003c\/font\u003e\u003c\/strong\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e\u003cp\u003eList of Contributors xvii\u003c\/p\u003e \u003cp\u003ePreface xxi\u003c\/p\u003e \u003cp\u003e\u003cb\u003eSection I Fundamentals in Human–AI Collaboration 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Human Interaction with Intelligent Automation: A Continuum of AI Levels from Which Designers and Users Can Choose 3\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eThomas B. Sheridan and William B. Rouse\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e1.1 Introduction 3\u003c\/p\u003e \u003cp\u003e1.2 Background 3\u003c\/p\u003e \u003cp\u003e1.2.1 Traditional Automation 3\u003c\/p\u003e \u003cp\u003e1.2.2 Human Interaction with Traditional Automation 4\u003c\/p\u003e \u003cp\u003e1.2.3 Artificial Intelligence 6\u003c\/p\u003e \u003cp\u003e1.2.3.1 Evolution of AI 6\u003c\/p\u003e \u003cp\u003e1.2.3.2 Spectrum of AI 7\u003c\/p\u003e \u003cp\u003e1.3 Human Interaction with Intelligent Automation 7\u003c\/p\u003e \u003cp\u003e1.3.1 AI for Robotic Automation 8\u003c\/p\u003e \u003cp\u003e1.3.2 Augmentation and Adaptation 8\u003c\/p\u003e \u003cp\u003e1.3.3 Levels of AI Assist in Automation 9\u003c\/p\u003e \u003cp\u003e1.3.4 Policy Issues Regarding AI and Automation 10\u003c\/p\u003e \u003cp\u003e1.4 Three Scenarios 10\u003c\/p\u003e \u003cp\u003e1.4.1 Driverless Cars 10\u003c\/p\u003e \u003cp\u003e1.4.2 Management of Autonomous Airplanes 11\u003c\/p\u003e \u003cp\u003e1.4.3 Healthcare Decision Support 11\u003c\/p\u003e \u003cp\u003e1.4.4 Comparison of Scenarios 13\u003c\/p\u003e \u003cp\u003e1.5 Discussion 13\u003c\/p\u003e \u003cp\u003e1.5.1 Continuum of Levels 13\u003c\/p\u003e \u003cp\u003e1.5.2 Transition Management 14\u003c\/p\u003e \u003cp\u003e1.5.3 Human-Centered Intelligent Automation 15\u003c\/p\u003e \u003cp\u003e1.6 Conclusions 15\u003c\/p\u003e \u003cp\u003eReferences 16\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Human–AI Interaction Fundamentals 17\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eGeorge Margetis, Stavroula Ntoa, Asterios Leonidis, and Constantine Stephanidis\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e2.1 Introduction 17\u003c\/p\u003e \u003cp\u003e2.2 Human-Centered AI 18\u003c\/p\u003e \u003cp\u003e2.2.1 Explainability and Understandability 18\u003c\/p\u003e \u003cp\u003e2.2.2 Equity and Fairness 19\u003c\/p\u003e \u003cp\u003e2.2.3 Human–AI Collaboration 20\u003c\/p\u003e \u003cp\u003e2.2.4 Ethical Considerations 20\u003c\/p\u003e \u003cp\u003e2.3 Interaction Styles 22\u003c\/p\u003e \u003cp\u003e2.3.1 Traditional Interaction Styles in AI 22\u003c\/p\u003e \u003cp\u003e2.3.2 Natural Language Interaction 23\u003c\/p\u003e \u003cp\u003e2.3.3 Gesture-Based Interaction 23\u003c\/p\u003e \u003cp\u003e2.3.4 Multimodal Interaction Models and Future Trends 24\u003c\/p\u003e \u003cp\u003e2.4 Interaction Contexts 25\u003c\/p\u003e \u003cp\u003e2.4.1 Affective Computing and Interaction with AI 25\u003c\/p\u003e \u003cp\u003e2.4.2 Decision-Making and Recommender Systems 25\u003c\/p\u003e \u003cp\u003e2.4.3 Generative AI and Large Language Models 26\u003c\/p\u003e \u003cp\u003e2.4.4 Human–Robot Interaction 26\u003c\/p\u003e \u003cp\u003e2.4.5 Application Domains 27\u003c\/p\u003e \u003cp\u003e2.5 HCI Aspects in Human–AI Interaction 28\u003c\/p\u003e \u003cp\u003e2.5.1 Usability and User Experience 28\u003c\/p\u003e \u003cp\u003e2.5.2 Cognitive and Social Factors 29\u003c\/p\u003e \u003cp\u003e2.5.3 Errors and User Trust 29\u003c\/p\u003e \u003cp\u003e2.5.4 Challenges and Limitations 30\u003c\/p\u003e \u003cp\u003e2.6 Summary and Conclusions 31\u003c\/p\u003e \u003cp\u003eReferences 31\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Human–AI Interaction Fundamentals 41\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eWoei-Chyi Chang, Sogand Hasanzadeh, and Vincent G. Duffy\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e3.1 Introduction 41\u003c\/p\u003e \u003cp\u003e3.2 Entities Involved in Human–AI Interaction 41\u003c\/p\u003e \u003cp\u003e3.2.1 Humans 41\u003c\/p\u003e \u003cp\u003e3.2.1.1 Demographic and Dispositional Factors 42\u003c\/p\u003e \u003cp\u003e3.2.1.2 Situational and Learned Factors 42\u003c\/p\u003e \u003cp\u003e3.2.2 Ai 43\u003c\/p\u003e \u003cp\u003e3.2.3 Environments 43\u003c\/p\u003e \u003cp\u003e3.3 Human–AI Interaction Levels 44\u003c\/p\u003e \u003cp\u003e3.3.1 Coexistence 44\u003c\/p\u003e \u003cp\u003e3.3.2 Cooperation 44\u003c\/p\u003e \u003cp\u003e3.3.3 Collaboration 45\u003c\/p\u003e \u003cp\u003e3.4 Critical Elements of Human–AI Interaction 45\u003c\/p\u003e \u003cp\u003e3.4.1 Trust 45\u003c\/p\u003e \u003cp\u003e3.4.1.1 Trust Spectrum 46\u003c\/p\u003e \u003cp\u003e3.4.1.2 Trust Calibration 46\u003c\/p\u003e \u003cp\u003e3.4.1.3 Trust Measurements 47\u003c\/p\u003e \u003cp\u003e3.4.2 Communication 48\u003c\/p\u003e \u003cp\u003e3.4.3 Privacy and Data Security 49\u003c\/p\u003e \u003cp\u003e3.4.4 Personalization and Adaptability 50\u003c\/p\u003e \u003cp\u003e3.5 Conclusions 51\u003c\/p\u003e \u003cp\u003eReferences 51\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Guidelines for Human–AI Interaction and User Experience 57\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eHelmut Degen\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e4.1 Introduction 57\u003c\/p\u003e \u003cp\u003e4.2 Social Needs 59\u003c\/p\u003e \u003cp\u003e4.2.1 Laws and Regulations from Selected Regions and Countries 60\u003c\/p\u003e \u003cp\u003e4.2.2 EU AI Act 60\u003c\/p\u003e \u003cp\u003e4.2.3 Chinese Artificial Intelligence Laws 62\u003c\/p\u003e \u003cp\u003e4.2.4 US AI Executive Order 14110 62\u003c\/p\u003e \u003cp\u003e4.2.5 Common Social Needs 63\u003c\/p\u003e \u003cp\u003e4.3 Guidelines to Address Selected Common Social Needs 65\u003c\/p\u003e \u003cp\u003e4.3.1 Ethical Use 65\u003c\/p\u003e \u003cp\u003e4.3.1.1 Social Need Details 65\u003c\/p\u003e \u003cp\u003e4.3.1.2 Guidelines 66\u003c\/p\u003e \u003cp\u003e4.3.2 Product Quality Over Product Lifecycle 66\u003c\/p\u003e \u003cp\u003e4.3.2.1 Social Need Details 66\u003c\/p\u003e \u003cp\u003e4.3.2.2 Guidelines 67\u003c\/p\u003e \u003cp\u003e4.3.3 Human Oversight 69\u003c\/p\u003e \u003cp\u003e4.3.3.1 Social Need Details 69\u003c\/p\u003e \u003cp\u003e4.3.4 Effective, Ethical, Robustness, Accuracy, Safety, and Security 70\u003c\/p\u003e \u003cp\u003e4.3.4.1 Social Need Details 70\u003c\/p\u003e \u003cp\u003e4.3.4.2 Guidelines 71\u003c\/p\u003e \u003cp\u003e4.3.5 Explainability and Trustworthiness 71\u003c\/p\u003e \u003cp\u003e4.3.5.1 Social Need Details 71\u003c\/p\u003e \u003cp\u003e4.3.5.2 Guidelines 72\u003c\/p\u003e \u003cp\u003e4.3.6 Diversity, Fairness, Impartiality, Privacy, Health 77\u003c\/p\u003e \u003cp\u003e4.3.6.1 Social Need Details 77\u003c\/p\u003e \u003cp\u003e4.3.6.2 Guidelines 80\u003c\/p\u003e \u003cp\u003e4.4 Discussion and Future Outlook 81\u003c\/p\u003e \u003cp\u003e4.4.1 Discussion 81\u003c\/p\u003e \u003cp\u003e4.4.2 Outlook 82\u003c\/p\u003e \u003cp\u003e4.4.3 Acknowledgments 82\u003c\/p\u003e \u003cp\u003e4.4.4 Funding 83\u003c\/p\u003e \u003cp\u003e4.4.5 Conflict of Interest 83\u003c\/p\u003e \u003cp\u003eReferences 83\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Human Intelligence vs. Artificial Intelligence 91\u003cbr\u003e \u003c\/b\u003e\u003ci\u003ePeiran Liu and Denny Yu\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e5.1 Characterizing Similarities and Differences Today 91\u003c\/p\u003e \u003cp\u003e5.1.1 Historical Perspectives of Human Intelligence 91\u003c\/p\u003e \u003cp\u003e5.1.2 Measurement of Human Intelligence 93\u003c\/p\u003e \u003cp\u003e5.1.3 Background on Artificial Intelligence 94\u003c\/p\u003e \u003cp\u003e5.1.4 Similarity and Differences—A Brief Discussion 95\u003c\/p\u003e \u003cp\u003e5.2 Limitations in Human and Artificial Intelligence Today 96\u003c\/p\u003e \u003cp\u003e5.2.1 Perception: Sensory Systems and Sensors 96\u003c\/p\u003e \u003cp\u003e5.2.2 Cognition 98\u003c\/p\u003e \u003cp\u003e5.3 Trustworthiness 100\u003c\/p\u003e \u003cp\u003eReferences 102\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Advances in Human–AI Collaboration: Training with AI Support 107\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eYubin Xie, Siu Shing Man, Ronggang Zhou, and Alan Hoi Shou Chan\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e6.1 Introduction 107\u003c\/p\u003e \u003cp\u003e6.1.1 What is an AI Agent? 108\u003c\/p\u003e \u003cp\u003e6.1.1.1 Concept of AI Agent 108\u003c\/p\u003e \u003cp\u003e6.1.1.2 Type of AI Agent 108\u003c\/p\u003e \u003cp\u003e6.1.1.3 Characteristics of AI Agent 108\u003c\/p\u003e \u003cp\u003e6.1.2 Training with AI Support 109\u003c\/p\u003e \u003cp\u003e6.1.2.1 Background 109\u003c\/p\u003e \u003cp\u003e6.1.2.2 Definition of AI Training 109\u003c\/p\u003e \u003cp\u003e6.1.3 Advantages of Training with AI Support 110\u003c\/p\u003e \u003cp\u003e6.1.3.1 Efficiency 110\u003c\/p\u003e \u003cp\u003e6.1.3.2 Personalized 111\u003c\/p\u003e \u003cp\u003e6.1.3.3 Cost Saving 111\u003c\/p\u003e \u003cp\u003e6.1.3.4 Continuous Learning 111\u003c\/p\u003e \u003cp\u003e6.1.4 Challenges and Ethical Considerations in Training with AI Support 112\u003c\/p\u003e \u003cp\u003e6.1.4.1 Transparency and Interpretability 112\u003c\/p\u003e \u003cp\u003e6.1.4.2 Fairness and Bias 112\u003c\/p\u003e \u003cp\u003e6.1.4.3 Other Challenges 112\u003c\/p\u003e \u003cp\u003e6.2 Application of AI in Training 113\u003c\/p\u003e \u003cp\u003e6.2.1 Knowledge Base Construction 113\u003c\/p\u003e \u003cp\u003e6.2.1.1 Concept of Knowledge Base 113\u003c\/p\u003e \u003cp\u003e6.2.1.2 Foundation of Knowledge Base 114\u003c\/p\u003e \u003cp\u003e6.2.2 Training Needs Analysis 115\u003c\/p\u003e \u003cp\u003e6.2.3 Feedback on Training Results 116\u003c\/p\u003e \u003cp\u003e6.2.4 Algorithm Aversion and User Experience 116\u003c\/p\u003e \u003cp\u003e6.3 Case Studies of Training with AI Support 117\u003c\/p\u003e \u003cp\u003e6.3.1 Case Study 1 117\u003c\/p\u003e \u003cp\u003e6.3.2 Case Study 2 118\u003c\/p\u003e \u003cp\u003e6.4 Summary 118\u003c\/p\u003e \u003cp\u003eAcknowledgments 119\u003c\/p\u003e \u003cp\u003eReferences 119\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Human-AI Teaming 123\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eTianyi Yuan, Minqian Yang, Dian Yu, and Pei-Luen Patrick Rau\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e7.1 Theoretical Foundations 123\u003c\/p\u003e \u003cp\u003e7.1.1 Definitions 123\u003c\/p\u003e \u003cp\u003e7.1.2 History and Trends 124\u003c\/p\u003e \u003cp\u003e7.1.3 Theoretical Frameworks for Human–AI Teaming 124\u003c\/p\u003e \u003cp\u003e7.2 Decision-Making Process in Human–AI Teaming 126\u003c\/p\u003e \u003cp\u003e7.2.1 Division of Labor 126\u003c\/p\u003e \u003cp\u003e7.2.2 Attribution 127\u003c\/p\u003e \u003cp\u003e7.3 Communication in Human–AI Teaming 128\u003c\/p\u003e \u003cp\u003e7.3.1 Factors Influencing Communication in Human–AI Teaming 128\u003c\/p\u003e \u003cp\u003e7.3.2 Benefits and Challenges 129\u003c\/p\u003e \u003cp\u003e7.3.3 Communication Strategies in Human–AI Teaming 129\u003c\/p\u003e \u003cp\u003e7.4 Trust 130\u003c\/p\u003e \u003cp\u003e7.4.1 Factors Influencing Trust in Human–AI Teaming 130\u003c\/p\u003e \u003cp\u003e7.4.2 The Role of Trust in Human–AI Teaming 131\u003c\/p\u003e \u003cp\u003e7.4.3 Strategies to Enhance Trust in Human–AI Teaming 131\u003c\/p\u003e \u003cp\u003e7.5 Explainability and Explainable AI in Human–AI Teaming 132\u003c\/p\u003e \u003cp\u003e7.5.1 Human–AI Teaming and the Role of Explainability 132\u003c\/p\u003e \u003cp\u003e7.5.2 Challenges and Trends in Explainable AI for Human–AI Teaming 132\u003c\/p\u003e \u003cp\u003e7.6 Ethical and Social Implications 133\u003c\/p\u003e \u003cp\u003e7.6.1 Ethical Dimensions of Human–AI Teaming 133\u003c\/p\u003e \u003cp\u003e7.6.2 Social Implications of Human–AI Teaming 134\u003c\/p\u003e \u003cp\u003eReferences 135\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Human Teaming with Automation and Advanced Agents 143\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eBarrett S. Caldwell, Rua M. Williams, and C. Nuela Enebechi\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e8.1 Introduction 143\u003c\/p\u003e \u003cp\u003e8.2 Clarifying Automation and Autonomy 144\u003c\/p\u003e \u003cp\u003e8.3 Teaming Metaphors and Models 146\u003c\/p\u003e \u003cp\u003e8.4 Dynamics of Expertise and Function Allocation 148\u003c\/p\u003e \u003cp\u003e8.5 Challenges in Ascribing Trust Dynamics 151\u003c\/p\u003e \u003cp\u003e8.6 Impacts of Culture and Bias 152\u003c\/p\u003e \u003cp\u003e8.7 Conclusion: Design for Robust and Resilient Human-Agent Teams 154\u003c\/p\u003e \u003cp\u003eReferences 155\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Integrating Generative Design and Ergonomics: A Data-Driven Approach with Digital Manikins 159\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eH. Onan Demirel, Xingang Li, and Zhenghui Sha\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e9.1 Introduction 159\u003c\/p\u003e \u003cp\u003e9.2 Literature Review 161\u003c\/p\u003e \u003cp\u003e9.2.1 Data-Driven Generative Design Process 161\u003c\/p\u003e \u003cp\u003e9.2.2 Deep Learning Methods for Cross-Modal Tasks 162\u003c\/p\u003e \u003cp\u003e9.2.3 Computational Human Factors via Digital Human Modeling 162\u003c\/p\u003e \u003cp\u003e9.3 Methodology 163\u003c\/p\u003e \u003cp\u003e9.3.1 Human-Centered AI-Assisted Concept Generation 164\u003c\/p\u003e \u003cp\u003e9.3.2 Concept Evaluation Using Digital Human Modeling 164\u003c\/p\u003e \u003cp\u003e9.3.3 Iterative Concept Refinement and Concept Modification 164\u003c\/p\u003e \u003cp\u003e9.4 Result 165\u003c\/p\u003e \u003cp\u003e9.4.1 Human-Centered AI-Assisted Concept Generation 165\u003c\/p\u003e \u003cp\u003e9.4.2 Human-Centered AI-Assisted Concept Evaluation 166\u003c\/p\u003e \u003cp\u003e9.5 Discussions 167\u003c\/p\u003e \u003cp\u003e9.6 Conclusions 169\u003c\/p\u003e \u003cp\u003eReferences 169\u003c\/p\u003e \u003cp\u003eSection II Application of Human–AI Collaboration 173\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 AI-Enabled Accessible Travel in Autonomous Vehicles: Promises, Perceptions, and Prototypes 175\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eBrandon J. Pitts, Qiyue Wang, and Bradley S. Duerstock\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e10.1 Introduction 175\u003c\/p\u003e \u003cp\u003e10.2 Travel-Limiting Disabilities and Challenges with Current Transportation Systems 176\u003c\/p\u003e \u003cp\u003e10.2.1 Travelers with Disabilities 176\u003c\/p\u003e \u003cp\u003e10.2.2 Aging populations 177\u003c\/p\u003e \u003cp\u003e10.3 Development of Autonomous and Shared Vehicles 177\u003c\/p\u003e \u003cp\u003e10.4 Perceptions of AVs and SAVs 179\u003c\/p\u003e \u003cp\u003e10.4.1 Travelers with Disabilities 179\u003c\/p\u003e \u003cp\u003e10.4.1.1 Mobility Impairments 179\u003c\/p\u003e \u003cp\u003e10.4.1.2 Visual Impairments 180\u003c\/p\u003e \u003cp\u003e10.4.1.3 General (Other Types of Disabilities) 181\u003c\/p\u003e \u003cp\u003e10.4.2 Aging Populations 181\u003c\/p\u003e \u003cp\u003e10.5 Interactions with AVs and SAVs 182\u003c\/p\u003e \u003cp\u003e10.5.1 Travelers with Disabilities 183\u003c\/p\u003e \u003cp\u003e10.5.2 Aging Populations 184\u003c\/p\u003e \u003cp\u003e10.6 Technologies and AI Solutions to Overcome Barriers to Using AVs 185\u003c\/p\u003e \u003cp\u003e10.7 Case Study: The EASI RIDER Innovation 188\u003c\/p\u003e \u003cp\u003e10.8 Future Research and Development Needs 193\u003c\/p\u003e \u003cp\u003e10.9 Conclusion 193\u003c\/p\u003e \u003cp\u003eAcknowledgments 194\u003c\/p\u003e \u003cp\u003eReferences 194\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 Cobot: Collaborative Robots 201\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eli liu\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e11.1 The Origins and Development of Collaborative Robots 201\u003c\/p\u003e \u003cp\u003e11.1.1 Overview of Early Industrial Robots 201\u003c\/p\u003e \u003cp\u003e11.1.2 From Industrial Automation to Cobots 202\u003c\/p\u003e \u003cp\u003e11.1.3 The Birth and Evolution of Cobots 203\u003c\/p\u003e \u003cp\u003e11.2 Technical Foundations of Cobots 204\u003c\/p\u003e \u003cp\u003e11.2.1 What Is a Collaborative Robot? 204\u003c\/p\u003e \u003cp\u003e11.2.2 The Role of AI in Cobots 204\u003c\/p\u003e \u003cp\u003e11.2.3 Multimodal Perception Systems 205\u003c\/p\u003e \u003cp\u003e11.2.4 Human–Robot Interaction and Safety Design 206\u003c\/p\u003e \u003cp\u003e11.3 Application Scenarios of Cobots 207\u003c\/p\u003e \u003cp\u003e11.3.1 Cobot Applications in Manufacturing 207\u003c\/p\u003e \u003cp\u003e11.3.2 Cobots in Logistics and Warehousing 207\u003c\/p\u003e \u003cp\u003e11.3.3 Innovative Applications in Healthcare and Service Industries 209\u003c\/p\u003e \u003cp\u003e11.3.4 Cobots in Agriculture and Environmental Management 210\u003c\/p\u003e \u003cp\u003e11.4 The Future of Collaborative Robots 211\u003c\/p\u003e \u003cp\u003e11.4.1 The Integration of Humanoid Robots and Cobots 211\u003c\/p\u003e \u003cp\u003e11.4.2 Ethical, Legal, and Safety Challenges 212\u003c\/p\u003e \u003cp\u003e11.4.3 The Integration of Cobots with Industry 4.0 213\u003c\/p\u003e \u003cp\u003e11.5 Summary 214\u003c\/p\u003e \u003cp\u003eReferences 214\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 AI Chat-Based Customer Services and Systems 217\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eQin Gao and Jinhan Zhang\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e12.1 Introduction 217\u003c\/p\u003e \u003cp\u003e12.2 Development of AI Chatbots in Customer Services 218\u003c\/p\u003e \u003cp\u003e12.3 Customer Service Affordances of AI Chatbots 219\u003c\/p\u003e \u003cp\u003e12.4 Factors Affecting Consumer Experience with AI Chatbots 225\u003c\/p\u003e \u003cp\u003e12.4.1 Functional and Utilitarian Features 225\u003c\/p\u003e \u003cp\u003e12.4.2 Interaction Experience Features 225\u003c\/p\u003e \u003cp\u003e12.4.3 System Quality Features 227\u003c\/p\u003e \u003cp\u003e12.4.4 Individual Differences 228\u003c\/p\u003e \u003cp\u003e12.5 Impacts of AI Chatbots on Customer Service Experience 229\u003c\/p\u003e \u003cp\u003e12.6 AI Chatbot in Public Services 230\u003c\/p\u003e \u003cp\u003e12.7 Challenges of Using AI Chatbot 232\u003c\/p\u003e \u003cp\u003e12.7.1 Technical and Functional Challenges 232\u003c\/p\u003e \u003cp\u003e12.7.2 Data Security, Privacy, and Ethical Challenges 232\u003c\/p\u003e \u003cp\u003e12.7.3 Organizational and Implementation Challenges 232\u003c\/p\u003e \u003cp\u003e12.8 Conclusions 233\u003c\/p\u003e \u003cp\u003eReferences 233\u003c\/p\u003e \u003cp\u003e\u003cb\u003e13 AI in Healthcare and Medicine 239\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eTianrong Chen, Zhenzhen Xie, and Calvin Or\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e13.1 Introduction 239\u003c\/p\u003e \u003cp\u003e13.2 What Is AI? 240\u003c\/p\u003e \u003cp\u003e13.2.1 Definition and Scope of AI 240\u003c\/p\u003e \u003cp\u003e13.2.2 Advancements of AI 240\u003c\/p\u003e \u003cp\u003e13.2.3 Role of AI in Healthcare and Medicine 241\u003c\/p\u003e \u003cp\u003e13.3 AI in Healthcare 241\u003c\/p\u003e \u003cp\u003e13.3.1 AI in Chronic Disease Management 241\u003c\/p\u003e \u003cp\u003e13.3.2 AI in Home Rehabilitation 242\u003c\/p\u003e \u003cp\u003e13.3.3 AI in Fall Prevention and Detection 243\u003c\/p\u003e \u003cp\u003e13.3.4 AI in Mental Health Support 244\u003c\/p\u003e \u003cp\u003e13.4 AI in Medicine 245\u003c\/p\u003e \u003cp\u003e13.4.1 Diagnosis and Clinical Decision Support 245\u003c\/p\u003e \u003cp\u003e13.4.2 Medical Imaging 246\u003c\/p\u003e \u003cp\u003e13.4.3 Surgery 247\u003c\/p\u003e \u003cp\u003e13.4.4 Predictive Analytics 248\u003c\/p\u003e \u003cp\u003e13.4.5 Clinical Workflow Management 248\u003c\/p\u003e \u003cp\u003e13.5 Considerations and Challenges 249\u003c\/p\u003e \u003cp\u003e13.5.1 Data Privacy and Security 249\u003c\/p\u003e \u003cp\u003e13.5.2 Data Fragmentation and Limited Availability 250\u003c\/p\u003e \u003cp\u003e13.5.3 Transparency and Interpretability of AI 250\u003c\/p\u003e \u003cp\u003e13.5.4 Human–AI Interaction 250\u003c\/p\u003e \u003cp\u003e13.5.5 Regulatory Frameworks and Guidelines 251\u003c\/p\u003e \u003cp\u003e13.6 Future Trends and Opportunities 252\u003c\/p\u003e \u003cp\u003e13.7 Conclusions 252\u003c\/p\u003e \u003cp\u003eReferences 253\u003c\/p\u003e \u003cp\u003e\u003cb\u003e14 AI in Human Resource Management 263\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eXinyu Fu, Fiona Fui-Hoon Nah, Songbo Liu, Kairui Zhang, Zixin Huang, Ruilin Zheng, and Weiqi Xie\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e14.1 AI-Driven Human Resource Processes 264\u003c\/p\u003e \u003cp\u003e14.1.1 Human Resource Planning 264\u003c\/p\u003e \u003cp\u003e14.1.2 Recruitment and Selection 265\u003c\/p\u003e \u003cp\u003e14.1.3 Learning and Development 266\u003c\/p\u003e \u003cp\u003e14.1.4 Performance Management 268\u003c\/p\u003e \u003cp\u003e14.1.5 Compensation and Benefits 269\u003c\/p\u003e \u003cp\u003e14.1.6 Employee Relationship Management 270\u003c\/p\u003e \u003cp\u003e14.1.7 Summary 271\u003c\/p\u003e \u003cp\u003e14.2 How AI Reshapes Team Dynamics 272\u003c\/p\u003e \u003cp\u003e14.2.1 AI Reshapes the Teaming Process 272\u003c\/p\u003e \u003cp\u003e14.2.2 Impact of AI on Team Performance 273\u003c\/p\u003e \u003cp\u003e14.2.3 AI and Leadership in the Team 273\u003c\/p\u003e \u003cp\u003e14.2.4 Summary 274\u003c\/p\u003e \u003cp\u003e14.3 Ethical Considerations and Challenges 274\u003c\/p\u003e \u003cp\u003e14.3.1 Employees’ Attitudes Toward AI 274\u003c\/p\u003e \u003cp\u003e14.3.2 Employees’ Adaptation to AI 275\u003c\/p\u003e \u003cp\u003e14.3.3 Human–Machine Relationship in HRM 275\u003c\/p\u003e \u003cp\u003eReferences 276\u003c\/p\u003e \u003cp\u003e\u003cb\u003e15 Kansei Engineering: Current Challenges and Future Trends with Advances in Artificial Intelligence 287\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eQing-Xing Qu, Fu Guo, and Vincent G. Duffy\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e15.1 Introduction 287\u003c\/p\u003e \u003cp\u003e15.2 Advances in Intelligence Product Development 289\u003c\/p\u003e \u003cp\u003e15.3 Kansei Design in Different Interaction Modalities 292\u003c\/p\u003e \u003cp\u003e15.3.1 Kansei Design in Unimodal Interaction 292\u003c\/p\u003e \u003cp\u003e15.3.2 Kansei Design in Bimodal Interaction 293\u003c\/p\u003e \u003cp\u003e15.3.3 Kansei Design in Multimodal Interaction 293\u003c\/p\u003e \u003cp\u003e15.4 Challenges Encountered and Opportunities for Future Research 295\u003c\/p\u003e \u003cp\u003e15.5 Conclusions 296\u003c\/p\u003e \u003cp\u003eReferences 297\u003c\/p\u003e \u003cp\u003e\u003cb\u003e16 AI in Collaborative Writing 303\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eNina Jiang and Vincent G. Duffy\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e16.1 Introduction 303\u003c\/p\u003e \u003cp\u003e16.2 Literature Review 304\u003c\/p\u003e \u003cp\u003e16.2.1 Data Collection 304\u003c\/p\u003e \u003cp\u003e16.2.2 Trend Analysis 304\u003c\/p\u003e \u003cp\u003e16.2.3 Co-citation Analysis 304\u003c\/p\u003e \u003cp\u003e16.2.4 Timeline Analysis 306\u003c\/p\u003e \u003cp\u003e16.2.5 Word Cloud 308\u003c\/p\u003e \u003cp\u003e16.2.6 Geography Network 309\u003c\/p\u003e \u003cp\u003e16.3 Fundamentals in Machine Learning and Text Generation 309\u003c\/p\u003e \u003cp\u003e16.3.1 Language Model 309\u003c\/p\u003e \u003cp\u003e16.3.2 Natural Language Generation 310\u003c\/p\u003e \u003cp\u003e16.4 User Perception of the Machine Role 311\u003c\/p\u003e \u003cp\u003e16.5 Strategies for Moving Beyond Passive Writing Assistance 313\u003c\/p\u003e \u003cp\u003e16.6 Collaborative Writing Best Practice Examples 314\u003c\/p\u003e \u003cp\u003e16.7 Discussion 315\u003c\/p\u003e \u003cp\u003e16.7.1 Evolution of AI Tools for Collaborative Writing 315\u003c\/p\u003e \u003cp\u003e16.7.2 Impact of AI on Collaborative Writing 316\u003c\/p\u003e \u003cp\u003e16.7.3 Challenges and Limitations 316\u003c\/p\u003e \u003cp\u003e16.7.4 Future Directions 317\u003c\/p\u003e \u003cp\u003e16.8 Conclusion 318\u003c\/p\u003e \u003cp\u003eReferences 318\u003c\/p\u003e \u003cp\u003e\u003cb\u003e17 Addressing AI Vulnerabilities Through Human-Centered Approaches and Risk Frameworks 325\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eSarvesh Sawant, Aasish Bhanu, Beau G. Schelble, and Kapil Chalil Madathil\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e17.1 Introduction 325\u003c\/p\u003e \u003cp\u003e17.1.1 AI Vulnerabilities 325\u003c\/p\u003e \u003cp\u003e17.1.2 Frameworks for Identifying and Mitigating AI Vulnerabilities 327\u003c\/p\u003e \u003cp\u003e17.2 Types of AI Vulnerabilities 328\u003c\/p\u003e \u003cp\u003e17.2.1 Technical Vulnerabilities 329\u003c\/p\u003e \u003cp\u003e17.2.2 Human-Centered AI Vulnerabilities 330\u003c\/p\u003e \u003cp\u003e17.3 Mitigating AI Vulnerabilities Through Human-Centered Design 331\u003c\/p\u003e \u003cp\u003e17.3.1 Human-Centered Design Principles 331\u003c\/p\u003e \u003cp\u003e17.3.2 Transparency and Explainability in AI 332\u003c\/p\u003e \u003cp\u003e17.3.3 Human-in-the-Loop Design 333\u003c\/p\u003e \u003cp\u003e17.3.4 User Education and Training 333\u003c\/p\u003e \u003cp\u003e17.3.5 Ethical Considerations 334\u003c\/p\u003e \u003cp\u003e17.4 Summary 335\u003c\/p\u003e \u003cp\u003eReferences 335\u003c\/p\u003e \u003cp\u003eIndex 341\u003c\/p\u003e\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eSubject Areas: Computer science [\u003ca title=\"See our other books on Computer science\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Computer%20science%20%5BUY%5D%22\"\u003eUY\u003c\/a\u003e]\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003c\/font\u003e","brand":"Wiley","offers":[{"title":"Brand New","offer_id":52433238524184,"sku":"9781394266371","price":82.59,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0730\/2037\/5320\/files\/9781394266371.jpg?v=1784852646","url":"https:\/\/freshlyprintedbooks.co.uk\/products\/advances-in-human-ai-collaboration-hardback-9781394266371","provider":"Freshly Printed Books","version":"1.0","type":"link"}