Public trust and blame attribution in human-AI interactions: a comparison between air traffic control and vehicle driving - Peidong Mei; Richard Cannon; Jim Everett; Peng Liu; Edmond Awad (Transportation Research Interdisciplinary Perspectives)
Artificial Intelligence (AI) has potential to address the increasing demand for capacity in Air Traffic Control (ATC). However, its integration poses several challenges and requires deep understanding of public perception. Insights from the context of Autonomous Vehicles (AVs), in which more studies have been done, can inform such understanding. In this article, we investigate how the public perceives the automated future of ATC in close comparison to AVs. We conducted two studies to examine public trust and blame attribution toward human and AI operators in different Human-AI Interaction (HAI) models, covering three levels of automation (Level 0: AI tool, Level 3: AI trainee, and Level 5: AI manager). We also explored their perceptions of ATC and vehicle driving (VD) by using ten task-related measures (Familiarity, Expertise, Tech Awareness, Openness, Media Discourse, Stake, two measures of Uncertainty, Positive Safety, and Negative Safety) and five agent-related characteristics (Capability, Robustness, Predictability, Honesty and Cooperativeness). The results showed greater trust and less blame attributed to humans in both ATC and VD, except in the Level 3 AI trainee model where humans were blamed more than AI. We also found both similarities and differences in people’s perceptions of the two contexts. Our findings provide evidence-based insights into how the public attribute trust and blame to the operators in ATC and VD. These results will inform industries on the development and implementation of AI integration in aviation and advise policymakers in evaluating public opinion on AI regulation.