Citation

Zhai S, Wang L, Liu P. Accid. Anal. Prev. 2023; 188: e107096.

Copyright

(Copyright © 2023, Elsevier Publishing)

DOI

10.1016/j.aap.2023.107096

PMID

37148677

Abstract

Machines are empowered with ever-increasing agency and decision-making authority to augment or even replace humans in various settings, making responsibility attribution less straightforward when they cause harm. Focusing on their applications in transportation, we consider human judgments of responsibility for automated vehicle crashes through a cross-national survey (N = 1657) and design hypothetical crashes after the 2018 Uber automated vehicle crash reportedly caused by a distracted human driver and an inaccurate machine driver. We examine the association between automation level-the human and machine drivers have different levels of agency (i.e., the human as a supervisor, backup driver, and mere passenger, respectively)-and human responsibility through the lens of perceived human controllability. We show the negative association between automation level and human responsibility, partly mediated by perceived human controllability, regardless of the involved responsibility metric (rating and allocation), the nationality of the involved participant (China and South Korea), and crash severity (injury and fatality). When the human and machine drivers in a conditionally automated vehicle jointly cause a crash (e.g., the 2018 Uber crash), the human driver and car manufacturer are asked to share responsibility. Our findings imply that the driver-centric tort law needs to be control-centric. They offer insights for attributing human responsibility for crashes involving automated vehicles.

Language: en

Keywords

Traffic crash; Automated vehicle; Controllability; Responsibility attribution