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Advances in Human-AI Collaboration
Vincent G. Duffy (Edited by), Duffy (Author), Waldemar Karwowski (Edited by), Gavriel Salvendy (Edited by)
9781394266371, Wiley
Hardback, published 14 April 2026
368 pages
24.4 x 19.6 x 2.8 cm, 0.862 kg
Detailed guide on how humans and AI systems work in tandem, focused on the successful deployment and use of applications Advances in Human-AI Collaboration 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. This 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. Written by a team of highly qualified academics with significant experience in the field, Advances in Human-AI Collaboration includes information on: Advances in Human-AI Collaboration 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.
List of Contributors xvii Preface xxi Section I Fundamentals in Human–AI Collaboration 1 1 Human Interaction with Intelligent Automation: A Continuum of AI Levels from Which Designers and Users Can Choose 3 1.1 Introduction 3 1.2 Background 3 1.2.1 Traditional Automation 3 1.2.2 Human Interaction with Traditional Automation 4 1.2.3 Artificial Intelligence 6 1.2.3.1 Evolution of AI 6 1.2.3.2 Spectrum of AI 7 1.3 Human Interaction with Intelligent Automation 7 1.3.1 AI for Robotic Automation 8 1.3.2 Augmentation and Adaptation 8 1.3.3 Levels of AI Assist in Automation 9 1.3.4 Policy Issues Regarding AI and Automation 10 1.4 Three Scenarios 10 1.4.1 Driverless Cars 10 1.4.2 Management of Autonomous Airplanes 11 1.4.3 Healthcare Decision Support 11 1.4.4 Comparison of Scenarios 13 1.5 Discussion 13 1.5.1 Continuum of Levels 13 1.5.2 Transition Management 14 1.5.3 Human-Centered Intelligent Automation 15 1.6 Conclusions 15 References 16 2 Human–AI Interaction Fundamentals 17 2.1 Introduction 17 2.2 Human-Centered AI 18 2.2.1 Explainability and Understandability 18 2.2.2 Equity and Fairness 19 2.2.3 Human–AI Collaboration 20 2.2.4 Ethical Considerations 20 2.3 Interaction Styles 22 2.3.1 Traditional Interaction Styles in AI 22 2.3.2 Natural Language Interaction 23 2.3.3 Gesture-Based Interaction 23 2.3.4 Multimodal Interaction Models and Future Trends 24 2.4 Interaction Contexts 25 2.4.1 Affective Computing and Interaction with AI 25 2.4.2 Decision-Making and Recommender Systems 25 2.4.3 Generative AI and Large Language Models 26 2.4.4 Human–Robot Interaction 26 2.4.5 Application Domains 27 2.5 HCI Aspects in Human–AI Interaction 28 2.5.1 Usability and User Experience 28 2.5.2 Cognitive and Social Factors 29 2.5.3 Errors and User Trust 29 2.5.4 Challenges and Limitations 30 2.6 Summary and Conclusions 31 References 31 3 Human–AI Interaction Fundamentals 41 3.1 Introduction 41 3.2 Entities Involved in Human–AI Interaction 41 3.2.1 Humans 41 3.2.1.1 Demographic and Dispositional Factors 42 3.2.1.2 Situational and Learned Factors 42 3.2.2 Ai 43 3.2.3 Environments 43 3.3 Human–AI Interaction Levels 44 3.3.1 Coexistence 44 3.3.2 Cooperation 44 3.3.3 Collaboration 45 3.4 Critical Elements of Human–AI Interaction 45 3.4.1 Trust 45 3.4.1.1 Trust Spectrum 46 3.4.1.2 Trust Calibration 46 3.4.1.3 Trust Measurements 47 3.4.2 Communication 48 3.4.3 Privacy and Data Security 49 3.4.4 Personalization and Adaptability 50 3.5 Conclusions 51 References 51 4 Guidelines for Human–AI Interaction and User Experience 57 4.1 Introduction 57 4.2 Social Needs 59 4.2.1 Laws and Regulations from Selected Regions and Countries 60 4.2.2 EU AI Act 60 4.2.3 Chinese Artificial Intelligence Laws 62 4.2.4 US AI Executive Order 14110 62 4.2.5 Common Social Needs 63 4.3 Guidelines to Address Selected Common Social Needs 65 4.3.1 Ethical Use 65 4.3.1.1 Social Need Details 65 4.3.1.2 Guidelines 66 4.3.2 Product Quality Over Product Lifecycle 66 4.3.2.1 Social Need Details 66 4.3.2.2 Guidelines 67 4.3.3 Human Oversight 69 4.3.3.1 Social Need Details 69 4.3.4 Effective, Ethical, Robustness, Accuracy, Safety, and Security 70 4.3.4.1 Social Need Details 70 4.3.4.2 Guidelines 71 4.3.5 Explainability and Trustworthiness 71 4.3.5.1 Social Need Details 71 4.3.5.2 Guidelines 72 4.3.6 Diversity, Fairness, Impartiality, Privacy, Health 77 4.3.6.1 Social Need Details 77 4.3.6.2 Guidelines 80 4.4 Discussion and Future Outlook 81 4.4.1 Discussion 81 4.4.2 Outlook 82 4.4.3 Acknowledgments 82 4.4.4 Funding 83 4.4.5 Conflict of Interest 83 References 83 5 Human Intelligence vs. Artificial Intelligence 91 5.1 Characterizing Similarities and Differences Today 91 5.1.1 Historical Perspectives of Human Intelligence 91 5.1.2 Measurement of Human Intelligence 93 5.1.3 Background on Artificial Intelligence 94 5.1.4 Similarity and Differences—A Brief Discussion 95 5.2 Limitations in Human and Artificial Intelligence Today 96 5.2.1 Perception: Sensory Systems and Sensors 96 5.2.2 Cognition 98 5.3 Trustworthiness 100 References 102 6 Advances in Human–AI Collaboration: Training with AI Support 107 6.1 Introduction 107 6.1.1 What is an AI Agent? 108 6.1.1.1 Concept of AI Agent 108 6.1.1.2 Type of AI Agent 108 6.1.1.3 Characteristics of AI Agent 108 6.1.2 Training with AI Support 109 6.1.2.1 Background 109 6.1.2.2 Definition of AI Training 109 6.1.3 Advantages of Training with AI Support 110 6.1.3.1 Efficiency 110 6.1.3.2 Personalized 111 6.1.3.3 Cost Saving 111 6.1.3.4 Continuous Learning 111 6.1.4 Challenges and Ethical Considerations in Training with AI Support 112 6.1.4.1 Transparency and Interpretability 112 6.1.4.2 Fairness and Bias 112 6.1.4.3 Other Challenges 112 6.2 Application of AI in Training 113 6.2.1 Knowledge Base Construction 113 6.2.1.1 Concept of Knowledge Base 113 6.2.1.2 Foundation of Knowledge Base 114 6.2.2 Training Needs Analysis 115 6.2.3 Feedback on Training Results 116 6.2.4 Algorithm Aversion and User Experience 116 6.3 Case Studies of Training with AI Support 117 6.3.1 Case Study 1 117 6.3.2 Case Study 2 118 6.4 Summary 118 Acknowledgments 119 References 119 7 Human-AI Teaming 123 7.1 Theoretical Foundations 123 7.1.1 Definitions 123 7.1.2 History and Trends 124 7.1.3 Theoretical Frameworks for Human–AI Teaming 124 7.2 Decision-Making Process in Human–AI Teaming 126 7.2.1 Division of Labor 126 7.2.2 Attribution 127 7.3 Communication in Human–AI Teaming 128 7.3.1 Factors Influencing Communication in Human–AI Teaming 128 7.3.2 Benefits and Challenges 129 7.3.3 Communication Strategies in Human–AI Teaming 129 7.4 Trust 130 7.4.1 Factors Influencing Trust in Human–AI Teaming 130 7.4.2 The Role of Trust in Human–AI Teaming 131 7.4.3 Strategies to Enhance Trust in Human–AI Teaming 131 7.5 Explainability and Explainable AI in Human–AI Teaming 132 7.5.1 Human–AI Teaming and the Role of Explainability 132 7.5.2 Challenges and Trends in Explainable AI for Human–AI Teaming 132 7.6 Ethical and Social Implications 133 7.6.1 Ethical Dimensions of Human–AI Teaming 133 7.6.2 Social Implications of Human–AI Teaming 134 References 135 8 Human Teaming with Automation and Advanced Agents 143 8.1 Introduction 143 8.2 Clarifying Automation and Autonomy 144 8.3 Teaming Metaphors and Models 146 8.4 Dynamics of Expertise and Function Allocation 148 8.5 Challenges in Ascribing Trust Dynamics 151 8.6 Impacts of Culture and Bias 152 8.7 Conclusion: Design for Robust and Resilient Human-Agent Teams 154 References 155 9 Integrating Generative Design and Ergonomics: A Data-Driven Approach with Digital Manikins 159 9.1 Introduction 159 9.2 Literature Review 161 9.2.1 Data-Driven Generative Design Process 161 9.2.2 Deep Learning Methods for Cross-Modal Tasks 162 9.2.3 Computational Human Factors via Digital Human Modeling 162 9.3 Methodology 163 9.3.1 Human-Centered AI-Assisted Concept Generation 164 9.3.2 Concept Evaluation Using Digital Human Modeling 164 9.3.3 Iterative Concept Refinement and Concept Modification 164 9.4 Result 165 9.4.1 Human-Centered AI-Assisted Concept Generation 165 9.4.2 Human-Centered AI-Assisted Concept Evaluation 166 9.5 Discussions 167 9.6 Conclusions 169 References 169 Section II Application of Human–AI Collaboration 173 10 AI-Enabled Accessible Travel in Autonomous Vehicles: Promises, Perceptions, and Prototypes 175 10.1 Introduction 175 10.2 Travel-Limiting Disabilities and Challenges with Current Transportation Systems 176 10.2.1 Travelers with Disabilities 176 10.2.2 Aging populations 177 10.3 Development of Autonomous and Shared Vehicles 177 10.4 Perceptions of AVs and SAVs 179 10.4.1 Travelers with Disabilities 179 10.4.1.1 Mobility Impairments 179 10.4.1.2 Visual Impairments 180 10.4.1.3 General (Other Types of Disabilities) 181 10.4.2 Aging Populations 181 10.5 Interactions with AVs and SAVs 182 10.5.1 Travelers with Disabilities 183 10.5.2 Aging Populations 184 10.6 Technologies and AI Solutions to Overcome Barriers to Using AVs 185 10.7 Case Study: The EASI RIDER Innovation 188 10.8 Future Research and Development Needs 193 10.9 Conclusion 193 Acknowledgments 194 References 194 11 Cobot: Collaborative Robots 201 11.1 The Origins and Development of Collaborative Robots 201 11.1.1 Overview of Early Industrial Robots 201 11.1.2 From Industrial Automation to Cobots 202 11.1.3 The Birth and Evolution of Cobots 203 11.2 Technical Foundations of Cobots 204 11.2.1 What Is a Collaborative Robot? 204 11.2.2 The Role of AI in Cobots 204 11.2.3 Multimodal Perception Systems 205 11.2.4 Human–Robot Interaction and Safety Design 206 11.3 Application Scenarios of Cobots 207 11.3.1 Cobot Applications in Manufacturing 207 11.3.2 Cobots in Logistics and Warehousing 207 11.3.3 Innovative Applications in Healthcare and Service Industries 209 11.3.4 Cobots in Agriculture and Environmental Management 210 11.4 The Future of Collaborative Robots 211 11.4.1 The Integration of Humanoid Robots and Cobots 211 11.4.2 Ethical, Legal, and Safety Challenges 212 11.4.3 The Integration of Cobots with Industry 4.0 213 11.5 Summary 214 References 214 12 AI Chat-Based Customer Services and Systems 217 12.1 Introduction 217 12.2 Development of AI Chatbots in Customer Services 218 12.3 Customer Service Affordances of AI Chatbots 219 12.4 Factors Affecting Consumer Experience with AI Chatbots 225 12.4.1 Functional and Utilitarian Features 225 12.4.2 Interaction Experience Features 225 12.4.3 System Quality Features 227 12.4.4 Individual Differences 228 12.5 Impacts of AI Chatbots on Customer Service Experience 229 12.6 AI Chatbot in Public Services 230 12.7 Challenges of Using AI Chatbot 232 12.7.1 Technical and Functional Challenges 232 12.7.2 Data Security, Privacy, and Ethical Challenges 232 12.7.3 Organizational and Implementation Challenges 232 12.8 Conclusions 233 References 233 13 AI in Healthcare and Medicine 239 13.1 Introduction 239 13.2 What Is AI? 240 13.2.1 Definition and Scope of AI 240 13.2.2 Advancements of AI 240 13.2.3 Role of AI in Healthcare and Medicine 241 13.3 AI in Healthcare 241 13.3.1 AI in Chronic Disease Management 241 13.3.2 AI in Home Rehabilitation 242 13.3.3 AI in Fall Prevention and Detection 243 13.3.4 AI in Mental Health Support 244 13.4 AI in Medicine 245 13.4.1 Diagnosis and Clinical Decision Support 245 13.4.2 Medical Imaging 246 13.4.3 Surgery 247 13.4.4 Predictive Analytics 248 13.4.5 Clinical Workflow Management 248 13.5 Considerations and Challenges 249 13.5.1 Data Privacy and Security 249 13.5.2 Data Fragmentation and Limited Availability 250 13.5.3 Transparency and Interpretability of AI 250 13.5.4 Human–AI Interaction 250 13.5.5 Regulatory Frameworks and Guidelines 251 13.6 Future Trends and Opportunities 252 13.7 Conclusions 252 References 253 14 AI in Human Resource Management 263 14.1 AI-Driven Human Resource Processes 264 14.1.1 Human Resource Planning 264 14.1.2 Recruitment and Selection 265 14.1.3 Learning and Development 266 14.1.4 Performance Management 268 14.1.5 Compensation and Benefits 269 14.1.6 Employee Relationship Management 270 14.1.7 Summary 271 14.2 How AI Reshapes Team Dynamics 272 14.2.1 AI Reshapes the Teaming Process 272 14.2.2 Impact of AI on Team Performance 273 14.2.3 AI and Leadership in the Team 273 14.2.4 Summary 274 14.3 Ethical Considerations and Challenges 274 14.3.1 Employees’ Attitudes Toward AI 274 14.3.2 Employees’ Adaptation to AI 275 14.3.3 Human–Machine Relationship in HRM 275 References 276 15 Kansei Engineering: Current Challenges and Future Trends with Advances in Artificial Intelligence 287 15.1 Introduction 287 15.2 Advances in Intelligence Product Development 289 15.3 Kansei Design in Different Interaction Modalities 292 15.3.1 Kansei Design in Unimodal Interaction 292 15.3.2 Kansei Design in Bimodal Interaction 293 15.3.3 Kansei Design in Multimodal Interaction 293 15.4 Challenges Encountered and Opportunities for Future Research 295 15.5 Conclusions 296 References 297 16 AI in Collaborative Writing 303 16.1 Introduction 303 16.2 Literature Review 304 16.2.1 Data Collection 304 16.2.2 Trend Analysis 304 16.2.3 Co-citation Analysis 304 16.2.4 Timeline Analysis 306 16.2.5 Word Cloud 308 16.2.6 Geography Network 309 16.3 Fundamentals in Machine Learning and Text Generation 309 16.3.1 Language Model 309 16.3.2 Natural Language Generation 310 16.4 User Perception of the Machine Role 311 16.5 Strategies for Moving Beyond Passive Writing Assistance 313 16.6 Collaborative Writing Best Practice Examples 314 16.7 Discussion 315 16.7.1 Evolution of AI Tools for Collaborative Writing 315 16.7.2 Impact of AI on Collaborative Writing 316 16.7.3 Challenges and Limitations 316 16.7.4 Future Directions 317 16.8 Conclusion 318 References 318 17 Addressing AI Vulnerabilities Through Human-Centered Approaches and Risk Frameworks 325 17.1 Introduction 325 17.1.1 AI Vulnerabilities 325 17.1.2 Frameworks for Identifying and Mitigating AI Vulnerabilities 327 17.2 Types of AI Vulnerabilities 328 17.2.1 Technical Vulnerabilities 329 17.2.2 Human-Centered AI Vulnerabilities 330 17.3 Mitigating AI Vulnerabilities Through Human-Centered Design 331 17.3.1 Human-Centered Design Principles 331 17.3.2 Transparency and Explainability in AI 332 17.3.3 Human-in-the-Loop Design 333 17.3.4 User Education and Training 333 17.3.5 Ethical Considerations 334 17.4 Summary 335 References 335 Index 341
Thomas B. Sheridan and William B. Rouse
George Margetis, Stavroula Ntoa, Asterios Leonidis, and Constantine Stephanidis
Woei-Chyi Chang, Sogand Hasanzadeh, and Vincent G. Duffy
Helmut Degen
Peiran Liu and Denny Yu
Yubin Xie, Siu Shing Man, Ronggang Zhou, and Alan Hoi Shou Chan
Tianyi Yuan, Minqian Yang, Dian Yu, and Pei-Luen Patrick Rau
Barrett S. Caldwell, Rua M. Williams, and C. Nuela Enebechi
H. Onan Demirel, Xingang Li, and Zhenghui Sha
Brandon J. Pitts, Qiyue Wang, and Bradley S. Duerstock
li liu
Qin Gao and Jinhan Zhang
Tianrong Chen, Zhenzhen Xie, and Calvin Or
Xinyu Fu, Fiona Fui-Hoon Nah, Songbo Liu, Kairui Zhang, Zixin Huang, Ruilin Zheng, and Weiqi Xie
Qing-Xing Qu, Fu Guo, and Vincent G. Duffy
Nina Jiang and Vincent G. Duffy
Sarvesh Sawant, Aasish Bhanu, Beau G. Schelble, and Kapil Chalil Madathil
Subject Areas: Computer science [UY]
