Building trust and responsibility into autonomous human-machine teams - Gillespie, Tony (Frontiers in Physics, 2022)
Harm can be caused to people and property by any highly-automated system, even with a human user, due to misuse or design; but which human has the legal liability for the consequences of the harm is not clear, or even which laws apply. The position is less clear for an interdependent Autonomous Human Machine Team System (A-HMT-S) which achieves its aim by reallocating tasks and resources between the human Team Leader and the Cyber Physical System (CPS). A-HMT-S are now feasible and may be the only solution for complex problems. However, legal authorities presume that humans are ultimately responsible for the actions of any automated system, including ones using Artificial Intelligence (AI) to replace human judgement. The concept of trust for an A-HMT-S using AI is examined in this paper with three critical questions being posed which must be addressed before an A-HMT-S can be trusted. A hierarchical system architecture is used to answer these questions, combined with a method to limit a node’s behaviour, ensuring actions requiring human judgement are referred to the user. The underpinning issues requiring Research and Development (R&D) for A-HMT-S applications are identified and where legal input is required to minimize financial and legal risk for all stakeholders. This work takes a step towards addressing the problems of developing autonomy for interdependent human-machine teams and systems.