Autonomous surgical robotic systems and the liability dilemma - Jamjoom, Aimun A.B.; Jamjoom, Ammer M.A.; Thomas, Jeffrey P.; Palmisciano, Paolo; Kerr, Karen; Collins, Justin W.; Vayena, Effy; Stoyanov, Danail; Marcus, Hani J.; , The iRobotSurgeon Collaboration (Frontiers in Surgery, 2022)
Background: Advances in machine learning and robotics have allowed the development of increasingly autonomous robotic systems which are able to make decisions and learn from experience. This distribution of decision-making away from human supervision poses a legal challenge for determining liability. Methods: The iRobotSurgeon survey aimed to explore public opinion towards the issue of liability with robotic surgical systems. The survey included five hypothetical scenarios where a patient comes to harm and the respondent needs to determine who they believe is most responsible: the surgeon, the robot manufacturer, the hospital, or another party. Results: A total of 2191 completed surveys were gathered evaluating 10955 individual scenario responses from 78 countries spanning 6 continents. The survey demonstrated a pattern in which participants were sensitive to shifts from fully surgeon-controlled scenarios to scenarios in which robotic systems played a larger role in decision-making such that surgeons were blamed less. However, there was a limit to this shift with human surgeons still being ascribed blame in scenarios of autonomous robotic systems where humans had no role in decision-making. Importantly, there was no clear consensus among respondents where to allocate blame in the case of harm occurring from a fully autonomous system. Conclusions: The iRobotSurgeon Survey demonstrated a dilemma among respondents on who to blame when harm is caused by a fully autonomous surgical robotic system. Importantly, it also showed that the surgeon is ascribed blame even when they have had no role in decision-making which adds weight to concerns that human operators could act as ‘moral crumple zones’ and bear the brunt of legal responsibility when a complex autonomous system causes harm.