\u003ci\u003eBi-directionality in Human-AI Collaborative Systems\u003c/i\u003e 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.\u003cbr\u003e\u003cbr\u003eIt 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.\u003cul\u003e\u003cli\u003eInvestigates the challenges in creating synergistic human and AI-based autonomous system-of-systems\u003c/li\u003e\u003cli\u003eIntegrates concepts from a wide range of disciplines, including applied and theoretical AI, quantum mechanics, social sciences, and systems engineering\u003c/li\u003e\u003cli\u003ePresents debates, models, and concepts of mutual dependency for autonomous human-machine teams, challenging assumptions across AI, systems engineering, data science, and quantum mechanics\u003c/li\u003e\u003c/ul\u003e
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