Bi-directionality in Human-AI Collaborative Systems investigates the foundations, metrics, and applications of human-machine systems, along with the legal ramifications of autonomy, including standards, trust by the public, and bidirectional trust by users and AI systems. The book 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. This is a valuable resource for world-class researchers and engineers who are theorizing on, designing, and developing 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.
1. 1: Introduction to bidirectionality in human–AI collaborative systems - p. 1 2. 2: Foundational approaches to post-hoc explainability for image classification - p. 23 3. 3: Explaining poisoned AI models - p. 55 4. 4: Desirability vs. feasibility: a research through design inquiry of explainable AI - p. 75 5. 5: Credition, uncertainty, consciousness, and communication - p. 119 6. 6: On the principles and effectiveness of gamification in bidirectional artificial intelligence and explainable AI - p. 135 7. 7: Employing Kolmogorov–Arnold network for man–machine collaboration - p. 161 8. 8: Collaborative communication for unnamable risks: a creative writing approach to aligning human–machine situation models in an open world - p. 193 9. 9: Not all explanations are created equal: investigating the pitfalls of current XAI evaluation - p. 227 10. 10: A mixture-of-experts flock: examining expert influence - p. 253 11. 11: On replacing humans with human simulators in human-in-the-loop systems built to interact with humans - p. 287 12. 12: Addressing procrastination and improving task completion efficiency through agent-based interventions - p. 303 13. 13: Navigating the sociotechnical labyrinth: dynamic certification for responsible embodied AI - p. 333 14. 14: Searching XAI collaborating with manager: bidirectional learning for human-tech applications - p. 349 15. 15: Natural perception-based control types for human/machine systems - p. 365 16. 16: Hybrid forums as a means to perceive bidirectional risks - p. 385 17. 17: Credit assignment: challenges and opportunities in developing human-like learning agents - p. 407 18. 18: Human–machine teams: advantages afforded by the quantum-likeness of interdependence - p. 451 19. Index - p. 475
Bi-directionality in Human-AI Collaborative Systems investigates the foundations, metrics, and applications of human-machine systems, along with the legal ramifications of autonomy, including standards, trust by the public, and bidirectional trust by users and AI systems. The book 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. This is a valuable resource for world-class researchers and engineers who are theorizing on, designing, and developing 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.
1. 1: Introduction to bidirectionality in human–AI collaborative systems - p. 1 2. 2: Foundational approaches to post-hoc explainability for image classification - p. 23 3. 3: Explaining poisoned AI models - p. 55 4. 4: Desirability vs. feasibility: a research through design inquiry of explainable AI - p. 75 5. 5: Credition, uncertainty, consciousness, and communication - p. 119 6. 6: On the principles and effectiveness of gamification in bidirectional artificial intelligence and explainable AI - p. 135 7. 7: Employing Kolmogorov–Arnold network for man–machine collaboration - p. 161 8. 8: Collaborative communication for unnamable risks: a creative writing approach to aligning human–machine situation models in an open world - p. 193 9. 9: Not all explanations are created equal: investigating the pitfalls of current XAI evaluation - p. 227 10. 10: A mixture-of-experts flock: examining expert influence - p. 253 11. 11: On replacing humans with human simulators in human-in-the-loop systems built to interact with humans - p. 287 12. 12: Addressing procrastination and improving task completion efficiency through agent-based interventions - p. 303 13. 13: Navigating the sociotechnical labyrinth: dynamic certification for responsible embodied AI - p. 333 14. 14: Searching XAI collaborating with manager: bidirectional learning for human-tech applications - p. 349 15. 15: Natural perception-based control types for human/machine systems - p. 365 16. 16: Hybrid forums as a means to perceive bidirectional risks - p. 385 17. 17: Credit assignment: challenges and opportunities in developing human-like learning agents - p. 407 18. 18: Human–machine teams: advantages afforded by the quantum-likeness of interdependence - p. 451 19. Index - p. 475
