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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.
