There exists considerable research concerning how humans attribute fault to each other, both in cases of accidents and those instances of intentional harm. There also exist studies involving blame attribution towards robots, when such robots have caused harm through operational failure or lack of safety features. However, relatively little work has, to date, examined the ways in which fault is attributed to self-driving vehicles involved in collisions, despite many newspaper and popular articles which both report past incidents and warn of future risk. This dissertation examined fault attribution in collisions involving autonomous vehicles by conducting three separate experiments. The first experiment placed participants in the roles of witnesses to a collision, and compared fault attributed to an autonomous vehicle to fault attributed to a regular, manually-operated vehicle, when those cars were involved in identical collisions. The second, and third experiments explored the fault that operators attributed to both themselves and autonomous vehicles when involved in a collision, whether they were the operator of the autonomous vehicle or the operator of a regular car that shared the road with automated ones. Results showed that, across experiments, perceived avoidability of the collision was the largest predictor of fault regardless of whether the participant was a witness or a driver. Additionally, participants in all three experiments thought themselves in general to be better than average drivers.
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Doctor of Philosophy (Ph.D.)
College of Sciences
Psychology; Human Factors Cognitive Psychology
Length of Campus-only Access
Doctoral Dissertation (Open Access)
Kaplan, Alexandra, "Determining and Assessing Fault Attribution in Collisions Involving Autonomous Vehicles" (2020). Electronic Theses and Dissertations, 2020-. 804.