Providing a high level of autonomy for a human-machine team requires assumptions that address behavior and mutual trust. The performance of a human-machine team is maximized when the partnership provides mutual benefits that satisfy design rationales, balance of control, and the nature of autonomy. The distinctively different characteristics and features of humans and machines are likely why they have the potential to work well together, overcoming each other's weaknesses through cooperation, synergy, and interdependence which forms a “collective intelligence. Trust is bidirectional and two-sided; humans need to trust AI technology, but future AI technology may also need to trust humans.Putting AI in the Critical Loop: Assured Trust and Autonomy in Human-Machine Teams focuses on human-machine trust and “assured performance and operation in order to realize the potential of autonomy. This book aims to take on the primary challenges of bidirectional trust and performance of autonomous systems, providing readers with a review of the latest literature, the science of autonomy, and a clear path towards the autonomy of human-machine teams and systems. Throughout this book, the intersecting themes of collective intelligence, bidirectional trust, and continual assurance form the challenging and extraordinarily interesting themes which will help lay the groundwork for the audience to not only bridge the knowledge gaps, but also to advance this science to develop better solutions.
1. 1 : Introduction - p. Page x 2. 2 : Alternative paths to developing engineering solutions for human-machine teams - p. Page 7 3. 3 : Risk determination vs risk perception: From hate speech, an erroneous drone attack, and military nuclear wastes to human-machine autonomy - p. Page 18 4. 4 : Appropriate context-dependent artificial trust in human-machine teamwork⁎ - p. Page 36 5. 5 : Toward a causal modeling approach for trust-based interventions in human-autonomy teams⁎ - p. Page 56 6. 6 : Risk management in human-in-the-loop AI-assisted attention aware systems - p. Page 76 7. 7 : Enabling trustworthiness in human-swarm systems through a digital twin - p. Page 91 8. 8 : Building trust with the ethical affordances of education technologies: A sociotechnical systems perspective - p. Page 119 9. 9 : Perceiving a humorous robot as a social partner - p. Page 154 10. 10 : Real-time AI: Using AI on the tactical edge - p. Page 181 11. 11 : Building a trustworthy digital twin: A brave new world of human machine teams and autonomous biological internet of things (BIoT) - p. Page 216 12. 12 : A framework of human factors methods for safe, ethical, and usable artificial intelligence in defense - p. Page 240 13. 13 : A schema for harms-sensitive reasoning, and an approach to populate its ontology by human annotation - p. Page 261 14. Index - p. Page 276
Providing a high level of autonomy for a human-machine team requires assumptions that address behavior and mutual trust. The performance of a human-machine team is maximized when the partnership provides mutual benefits that satisfy design rationales, balance of control, and the nature of autonomy. The distinctively different characteristics and features of humans and machines are likely why they have the potential to work well together, overcoming each other's weaknesses through cooperation, synergy, and interdependence which forms a “collective intelligence. Trust is bidirectional and two-sided; humans need to trust AI technology, but future AI technology may also need to trust humans.Putting AI in the Critical Loop: Assured Trust and Autonomy in Human-Machine Teams focuses on human-machine trust and “assured performance and operation in order to realize the potential of autonomy. This book aims to take on the primary challenges of bidirectional trust and performance of autonomous systems, providing readers with a review of the latest literature, the science of autonomy, and a clear path towards the autonomy of human-machine teams and systems. Throughout this book, the intersecting themes of collective intelligence, bidirectional trust, and continual assurance form the challenging and extraordinarily interesting themes which will help lay the groundwork for the audience to not only bridge the knowledge gaps, but also to advance this science to develop better solutions.
1. 1 : Introduction - p. Page x 2. 2 : Alternative paths to developing engineering solutions for human-machine teams - p. Page 7 3. 3 : Risk determination vs risk perception: From hate speech, an erroneous drone attack, and military nuclear wastes to human-machine autonomy - p. Page 18 4. 4 : Appropriate context-dependent artificial trust in human-machine teamwork⁎ - p. Page 36 5. 5 : Toward a causal modeling approach for trust-based interventions in human-autonomy teams⁎ - p. Page 56 6. 6 : Risk management in human-in-the-loop AI-assisted attention aware systems - p. Page 76 7. 7 : Enabling trustworthiness in human-swarm systems through a digital twin - p. Page 91 8. 8 : Building trust with the ethical affordances of education technologies: A sociotechnical systems perspective - p. Page 119 9. 9 : Perceiving a humorous robot as a social partner - p. Page 154 10. 10 : Real-time AI: Using AI on the tactical edge - p. Page 181 11. 11 : Building a trustworthy digital twin: A brave new world of human machine teams and autonomous biological internet of things (BIoT) - p. Page 216 12. 12 : A framework of human factors methods for safe, ethical, and usable artificial intelligence in defense - p. Page 240 13. 13 : A schema for harms-sensitive reasoning, and an approach to populate its ontology by human annotation - p. Page 261 14. Index - p. Page 276
