Generative AI Risks and Benefits within Human-Machine Teams

William Lawless, Marco Brambilla

• Publisher: Academic Press
• Year: 2026
• Topic: Design
• Subtopic: Human-Computer Interaction
• eBook ISBN:
  9780443516269

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. - Explores core concepts and practical applications of human-machine collaboration - Addresses ethical, legal, and trust issues in generative AI development - Offers insights into adaptive collaboration patterns and teamwork support - Provides frameworks for integrating generative AI within autonomous systems

Table of contents

• Title of Book
• 1. Introduction: Generative AI risks and benefits within human–machine teams
• 2. Conversational agents for energy efficiency: Methods, architectures, and real-world applications
• 3. A knowledge of software behavior and curvature
• 4. Humans–AI-based communication and reasoning in complex, risky adversarial domains
• 5. Evolution of A4L: A data architecture for AI-augmented learning
• 6. Using natural language for human-robot collaboration in the real world
• 7. Human and large language model workflows for engineering open-world enterprise dynamics
• 8. The role of generative AI in human-AI collaboration in the fashion design process
• 9. Coexisting with AI: Teaching ourselves to be innovators in information evolution (Panevolution)
• 10. The effect of cascades on human-machine-AI-team communications
• 11. Human-AI collaboration for trust management
• 12. IPSO: A generalized framework for examining team effectiveness through team inputs, processes, states, and outcomes
• 13. AI fluidity and AI Act regulation: Goliath and David in human-centric risk management
• 14. Toward human-AI partnership: From tools to teammates
• 15. Synergistic pedagogy: Integrating AI collaborators into data science education
• Index