@article{ref1,
title="Machines versus humans: people's biased responses to traffic accidents involving self-driving vehicles",
journal="Accident analysis and prevention",
year="2019",
author="Liu, Peng and Du, Yong and Xu, Zhigang",
volume="125",
number="",
pages="232-240",
abstract="Although self-driving vehicles (SDVs) bring with them the promise of improved traffic safety, they cannot eliminate all crashes. Little is known about whether people respond crashes involving SDVs and human drivers differently and why. Across five vignette-based experiments in two studies (total N = 1267), for the first time, we witnessed that participants had a tendency to perceive traffic crashes involving SDVs to be more severe than those involving conventionally human-driven vehicles (HDVs) regardless of their severity (injury or fatality) or cause (SDVs/HDVs or others). Furthermore, we found that this biased response could be a result of people's reliance on the affect heuristic. More specifically, higher prior negative affect tagged with an SDV (vs. an HDV) intensifies people's negative affect evoked by crashes involving the SDV (vs. those involving the HDV), which subsequently results in higher perceived severity and lower acceptability of the crash. Our results imply that people's over-reaction to crashes involving SDVs may be a psychological barrier to their adoption and that we may need to forestall a less stringent introduction policy that allows SDVs on public roads as it may lead to more crashes that could possibly deter people from adopting SDVs. We discuss other theoretical and practical implications of our results and suggest potential approaches to de-biasing people's responses to crashes involving SDVs.<br><br>Copyright © 2019 Elsevier Ltd. All rights reserved.<p /> <p>Language: en</p>",
language="en",
issn="0001-4575",
doi="10.1016/j.aap.2019.02.012",
url="http://dx.doi.org/10.1016/j.aap.2019.02.012"
}