Interdependent Human-Machine Teams: The Path to Autonomy examines the foundations, metrics, and applications of human-machine systems, the legal ramifications of autonomy, trust by the public, and trust by the users and AI systems of their users, integrating concepts from various disciplines such as AI, machine learning, social sciences, quantum mechanics, and systems engineering. In this book, world-class researchers, engineers, ethicists, and social scientists discuss what machines, humans, and systems should discuss with each other, to policymakers, and to the public. It establishes the meaning and operation of “shared contexts” between humans and machines, policy makers, and the public and explores how human-machine systems affect targeted audiences (researchers, machines, robots, users, regulators, etc.) and society, as well as future ecosystems composed of humans, machines, and systems.
1. 1. Introduction to “autonomous human–machine teams” - p. 1 2. 2. Toward a new foundation for AI - p. 15 3. 3. Human–machine teaming using large language models∗ - p. 41 4. 4. Development of a team cohesion scale for use in human-autonomy team research - p. 67 5. 5. Enabling human–machine symbiosis: Automated establishment of common ground and estimates of the topological structures of Commander’s Intent - p. 101 6. 6. Measuring consequential changes in human-autonomous system interactions - p. 121 7. 7. User affordances to engineer open-world enterprise dynamics - p. 145 8. 8. Truth-O-Meter: Collaborating with LLM in fighting its hallucinations - p. 175 9. 9. Natural versus artificial intelligence: AI insights from the cognitive sciences - p. 211 10. 10. Intention when humans team with AI - p. 235 11. 11. Autonomy: A family resemblance concept? An exploration of human–robot teams - p. 257 12. 12. A theoretical approach to management of limited attentional resources to support the m:N operation in advanced air mobility ecosystem - p. 273 13. 13. Predicting workload of dispatchers supervising autonomous systems - p. 293 14. 14. The generative AI weapon of mass destruction: Evolving disinformation threats, vulnerabilities, and mitigation frameworks - p. 315 15. 15. Ethics for artificial agents: Toward commensurate capability and self-regulation - p. 349 16. 16. Self-visualization for the human–machine mind–body problem - p. 373 17. 17. Knowledge, consciousness, and debate: advancing the science of autonomous human–machine teams - p. 403 18. Index - p. 425
Interdependent Human-Machine Teams: The Path to Autonomy examines the foundations, metrics, and applications of human-machine systems, the legal ramifications of autonomy, trust by the public, and trust by the users and AI systems of their users, integrating concepts from various disciplines such as AI, machine learning, social sciences, quantum mechanics, and systems engineering. In this book, world-class researchers, engineers, ethicists, and social scientists discuss what machines, humans, and systems should discuss with each other, to policymakers, and to the public. It establishes the meaning and operation of “shared contexts” between humans and machines, policy makers, and the public and explores how human-machine systems affect targeted audiences (researchers, machines, robots, users, regulators, etc.) and society, as well as future ecosystems composed of humans, machines, and systems.
1. 1. Introduction to “autonomous human–machine teams” - p. 1 2. 2. Toward a new foundation for AI - p. 15 3. 3. Human–machine teaming using large language models∗ - p. 41 4. 4. Development of a team cohesion scale for use in human-autonomy team research - p. 67 5. 5. Enabling human–machine symbiosis: Automated establishment of common ground and estimates of the topological structures of Commander’s Intent - p. 101 6. 6. Measuring consequential changes in human-autonomous system interactions - p. 121 7. 7. User affordances to engineer open-world enterprise dynamics - p. 145 8. 8. Truth-O-Meter: Collaborating with LLM in fighting its hallucinations - p. 175 9. 9. Natural versus artificial intelligence: AI insights from the cognitive sciences - p. 211 10. 10. Intention when humans team with AI - p. 235 11. 11. Autonomy: A family resemblance concept? An exploration of human–robot teams - p. 257 12. 12. A theoretical approach to management of limited attentional resources to support the m:N operation in advanced air mobility ecosystem - p. 273 13. 13. Predicting workload of dispatchers supervising autonomous systems - p. 293 14. 14. The generative AI weapon of mass destruction: Evolving disinformation threats, vulnerabilities, and mitigation frameworks - p. 315 15. 15. Ethics for artificial agents: Toward commensurate capability and self-regulation - p. 349 16. 16. Self-visualization for the human–machine mind–body problem - p. 373 17. 17. Knowledge, consciousness, and debate: advancing the science of autonomous human–machine teams - p. 403 18. Index - p. 425
