Proceedings of the Annual Meeting of the Cognitive Science Society

This study investigates how people assign blame to autonomous vehicles (AVs) when involved in an accident. Our experiment (N = 2647) revealed that people placed more blame on AVs than on human drivers when accident details were unspecified. To examine whether people assess major classes of blame-relevant information differently for AVs and humans, we developed a causal model and introduced a novel concept of prevention effort, which emerged as a crucial factor for blame judgement alongside intentionality. Finally, we addressed the “many hands” problem by exploring how people assign blame to entities associated with AVs and human drivers, such as the car company or an accident victim. Our findings showed that people assigned high blame to these entities in scenarios involving AVs, but not with human drivers. This necessitates adapting a model of blame for AVs to include other agents and thus allow for blame allocation “outside” of autonomous vehicles.