Identifying Factors that Influence Trust in Automated Cars and Medical Diagnosis Systems

摘要

Our research goals are to understand and model the factors that affect trust in automation across a variety of application domains. For the initial surveys described in this paper, we selected two domains: automotive and medical. Specifically, we focused on driverless cars (e.g., Google Cars) and automated medical diagnoses (e.g., IBM's Watson). There were two dimensions for each survey: the safety criticality of the situation in which the system was being used and name-brand recognizability. We designed the surveys and administered them electronically, using Survey Monkey and Amazon's Mechanical Turk. We then performed statistical analyses of the survey results to discover common factors across the domains, domain-specific factors, and implications of safety criticality and brand recognizability on trust factors. We found commonalities as well as dissimilarities in factors between the two domains, suggesting the possibility of creating a core model of trust that could be modified for individual domains. The results of our research will allow for the creation of design guidelines for autonomous systems that will be better accepted and used by target populations.