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Bi-directionality in Human-AI Collaborative Systems

Editat de William Lawless, Ranjeev Mittu, Donald Sofge, Marco Brambilla
en Limba Engleză Paperback – iul 2025
Recent advances in generative AI techniques such as Large Language Models have exacerbated the growing concerns associated with AI such as the risk, trust, and safety from the use of machines/AI in open situations. These concerns present major hurdles in the development of verified and validated engineered systems involving bi-directional pathways across the human-machine barrier; bi-directionality in this context means understanding the design and operational consequences of the human on the agent, and vice-versa. Current discussions on human-AI interactions are fragmented, focusing either on the impact of AI on human stakeholders (and relevant human factor considerations), or potential ways of involving humans in computational interventions (e.g., data annotation, behavior interpretation).

Bi-directionality in Human-AI Collaborative Systems investigates the foundations, metrics, and applications of human-machine systems; the legal ramifications of autonomy; standards, trust by the public, and bidirectional trust by the users and AI systems of their users. It addresses the challenges in creating synergistic human and AI-based autonomous system-of-systems by focusing on the underlying challenges associated with bi-directionality. Chapters cover advances in LLMs, logic, machine learning choices, the development of standards, as well as human-centered approaches to autonomous human-machine teams. The book is a valuable resource for world-class researchers and engineers who are theorizing about, designing, and operating the development of autonomous systems. It will also be useful for government scientists, business leaders, social scientists, philosophers, regulators and legal experts interested in the impact of autonomous human-machine teams and systems.

  • Investigates the challenges in creating synergistic human and AI-based autonomous system-of-systems
  • Integrates concepts from a wide range of disciplines including applied and theoretical AI, quantum mechanics, social sciences, and systems engineering
  • Presents debates, models, and concepts of mutual dependency for autonomous human-machine teams, challenging assumptions across AI, systems engineering, data science, and quantum mechanics
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Specificații

ISBN-13: 9780443405532
ISBN-10: 0443405530
Pagini: 300
Dimensiuni: 191 x 235 mm
Editura: ELSEVIER SCIENCE

Cuprins

1. Introduction
2. Interdependence in the human-machine fusion process
3. Advances in large language models
4. Logic applied to machines as part of a human-machine team
5. Machine learning model choices
6. Mixing machines and humans with mathematics
7. The development of standards for human-machine teams
8. The Systems Engineering Research Center’s approach to teams of swarms, machines and humans
9. Human-machine teams in aviation
10. Autonomous human-machine teams in Australia
11. A human-centered approach to autonomous human-machine teams
12. Risks and ethics in human-machine teams
13. Data Poisoning in human-machine teams
14. Trust and among human-machine teammates
15. Belief and consciousness in human-machine teams
16. Explainability in human-machine teams
17. Risk, trust, and safety in human-machine teams
18. Joint awareness in human-machine teams 19. Shared mental models in human-machine teams
20. System design and engineering for human-machine teams
21. Testing and evaluation of human-machine teams