Generative AI Risks and Benefits within Human-Machine Teams
Will be published approx. on 1. January 2027
Book
Paperback/Softback
450 pages
978-0-443-51625-2 (ISBN)
Description
Generative AI Risks and Benefits within Human-Machine Teams delves into the foundational principles, metrics, and applications of human-machine systems, addressing the legal ramifications of autonomy, public trust, and bidirectional AI systems. This book brings together world-class researchers, engineers, philosophers, social scientists, and other experts to discuss the critical aspects of generative AI, its risks, and benefits. The authors aim to establish a shared context between humans and machines, regulators, the public, and other stakeholders, exploring how these systems impact targeted audiences and society at large. The book combines human-centered computing and autonomous human-machine teams to provide a comprehensive understanding of generative AI's potential and challenges. The book is structured to guide readers through a detailed exploration of these topics. It begins with an introduction to the core concepts of human-machine collaboration and the next generation of large language models. The discussion then moves to practical applications, such as Human-AI collaboration for energy communities, autonomous human-machine team advances, and human-AI collaboration in the design process. The book also delves into adaptive collaboration patterns for logic modeling, assessing multimodal large language models in resolving visual ambiguities, and developing team context-aware collaborative AI assistants. Additionally, it explores the taxonomy of teamwork support for collaborative AI efforts, trust management in human-AI collaboration, and provides a distributed teaming testbed for human-machine collaboration in space missions. Finally, it addresses the limits of classical team science in interdependent human-machine teams. Generative AI Risks and Benefits within Human-Machine Teams is an essential resource for computer scientists and systems engineers focused on designing and theorizing about the development of autonomous systems. By providing in-depth insights into the integration of generative AI within human-machine teams, this book equips professionals with the knowledge to navigate the complexities of AI autonomy, enhance collaboration, and address the ethical and technical challenges associated with these advanced technologies.
More details
Language
English
Place of publication
San Diego
United States
Publishing group
Elsevier Science Publishing Co Inc
Target group
Professional and scholarly
Product notice
Paperback (trade)
Unsewn / adhesive bound
Dimensions
Height: 235 mm
Width: 191 mm
ISBN-13
978-0-443-51625-2 (9780443516252)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Persons
William Lawless is professor of mathematics and psychology at Paine College, GA. For his PhD topic on group dynamics, he theorized about the causes of tragic mistakes made by large organizations with world-class scientists and engineers. After his PhD in 1992, DOE invited him to join its citizens advisory board (CAB) at DOE's Savannah River Site (SRS), Aiken, SC. As a founding member, he coauthored numerous recommendations on environmental remediation from radioactive wastes (e.g., the regulated closure in 1997 of the first two high-level radioactive waste tanks in the USA). He is a member of INCOSE, IEEE, AAAI and AAAS. His research today is on autonomous human-machine teams (A-HMT). He is the lead editor of seven published books on artificial intelligence. He was lead organizer of a special issue on "human-machine teams and explainable AI? by AI Magazine (2019). He has authored over 85 articles and book chapters, and over 175 peer-reviewed proceedings. He was the lead organizer of twelve AAAI symposia at Stanford (2020). Since 2018, he has also been serving on the Office of Naval Research's Advisory Boards for the Science of Artificial Intelligence and Command Decision Making. Marco Brambilla is full professor at Politecnico di Milano. He is active in research and innovation, both at industrial and academic level. His research interests include data science, software modeling languages and design patterns, crowdsourcing, social media monitoring, and big data analysis. He has been visiting researcher at CISCO, San Jose, and University of California, San Diego. He has been visiting professor at Dauphine University, Paris. He is founder of various startups and spinoffs, including WebRatio, Fluxedo, and Quantia, focusing on social media analysis, software modeling, Mobile and Business Process based software applications, and data science projects. He is author of various international books including Model Driven Software Development in Practice (II edizione, Morgan-Claypool, 2017, adopted in 100+ universities worldwide), Web Information Retrieval (Springer, 2013), Interaction Flow Modeling Language (Morgan-Kauffman, 2014), Designing Data-Intensive Web Applications (Morgan-Kauffman, 2002). He also authored more than 250 research articles in top research journals and conferences. He was awarded various best paper awards and gave keynotes and speeches at many conferences and organisations. He is the main author of the OMG (Object Management Group) standard IFML (Interaction Flow Modeling Language). He participated in several European and international research projects. He has been reviewer of FP7 projects and evaluator of EU FP7 proposals, as well as of national and local government funding programmes throughout Europe. He has been PC chair of ICWE 2008 and ICWE 2021, as well as co-chair of various tracks, conferences and workshops. He is associate editor of various journals and PC member of several conferences and workshops.
Content
1. Introduction
2. Human-AI Collaboration for Energy Communities supported by an AI-based Conversational Agent
3. Enhancing Human-Autonomous System Interaction and Team Dynamics in Automated Driving Systems
4. Human and AI-Based Communication and Reasoning in Complex Adversarial Domains
5. Evolution of Data Architecture for AI-Augmented Learning
6. Human-Robot Collaboration Using Natural Language in the Read World
7. Human and Large Language Model Workflows for Engineering Open-Worl Enterprise Dynamics
8. A Human-Centered Comparative Study on LLMs in the Fashion Design Process
9. A Distributed Teaming Testbed for Human-Machine Collaboration in Futuristic Space Missions
10. Identifying uncertainty breakpoints for machine handoff to humans
11. The effect of cascades on human-machine-AI-team communications
12. Toward a generalized model for evaluating human-AI team effectiveness
13. Semantics of Foundation Models
14. Toward Human-AI Partnership: from tools to teammates
15. Synergistic Pedagogy: Integrating AI collaborators into Data Science Education
16. AI Fluidity and AI Act Regulation
17. Toward Human-Centric Adaptation: Bidrectional Feedback Loops in Human-Machine teams
18. Human AI Collaboration for Trust Management: Key Roles and Task Domains
19. Formally Situated Human-Machine Control Affordances
2. Human-AI Collaboration for Energy Communities supported by an AI-based Conversational Agent
3. Enhancing Human-Autonomous System Interaction and Team Dynamics in Automated Driving Systems
4. Human and AI-Based Communication and Reasoning in Complex Adversarial Domains
5. Evolution of Data Architecture for AI-Augmented Learning
6. Human-Robot Collaboration Using Natural Language in the Read World
7. Human and Large Language Model Workflows for Engineering Open-Worl Enterprise Dynamics
8. A Human-Centered Comparative Study on LLMs in the Fashion Design Process
9. A Distributed Teaming Testbed for Human-Machine Collaboration in Futuristic Space Missions
10. Identifying uncertainty breakpoints for machine handoff to humans
11. The effect of cascades on human-machine-AI-team communications
12. Toward a generalized model for evaluating human-AI team effectiveness
13. Semantics of Foundation Models
14. Toward Human-AI Partnership: from tools to teammates
15. Synergistic Pedagogy: Integrating AI collaborators into Data Science Education
16. AI Fluidity and AI Act Regulation
17. Toward Human-Centric Adaptation: Bidrectional Feedback Loops in Human-Machine teams
18. Human AI Collaboration for Trust Management: Key Roles and Task Domains
19. Formally Situated Human-Machine Control Affordances