This study conducted an experiment to test how the level of blame differs between an artificial intelligence (AI) and a human driver based on attribution theory and computers are social actors (CASA). It used a 2 (human vs. AI driver) x 2 (victim survived vs. victim died) x 2 (female vs. male driver) design. After reading a given scenario, participants (N = 284) were asked to assign a level of responsibility to the driver. The participants blamed drivers more when the driver was AI compared to when the driver was a human. Also, the higher level of blame was shown when the result was more severe. However, gender bias was found not to be significant when faulting drivers. These results indicate that the intention of blaming AI comes from the perception of dissimilarity and the seriousness of outcomes influences the level of blame. Implications of findings for applications and theory are discussed.

这项研究进行了一项实验，基于归因理论，测试了人工智能（AI）和人类驾驶员之间的责备程度如何不同，并且计算机是社会行为者（CASA）。它使用了2（人类与AI驾驶员）x 2（受害者幸存者与受害者死亡）x 2（女性与男性驾驶员）设计。阅读给定场景后，要求参与者（N = 284）为驾驶员分配责任级别。与驾驶员为人类相比，参与者将驾驶员更多地归咎于驾驶员。同样，当结果更为严重时，也要表现出更高的责备程度。但是，发现对驾驶员造成过失时性别偏见并不明显。这些结果表明，归咎于AI的意图来自对异同的感知，而结果的严重性会影响归咎水平。