Most accidents are caused by human error, simply because our brain cannot process as many inputs nor maintain attention for as long. Thus, autonomous vehicles will eventually make our roads safer.
However, people trust human drivers more than autonomous vehicles, reveals a new study published in the magazine Risk Analysis. This bias is called blame attribution asymmetry.
Risk analysis
Semi-autonomous vehicles (semi-AVs), which allow humans to supervise driving and take control of the vehicle, are already on the road. However, when there is an accident, we are inclined to think that the fault the machine or the vehicle manufacturer had it before the human driver.
Researchers led by Peng Liu, associate professor at the School of Management and Economics at Tianjin University, conducted experiments to measure participants' responses to hypothetical semi-AV shocks. When a crash was caused by a vehicle's automated system, participants assigned more blame and responsibility to the automation and its manufacturer and indicated that the victim should be compensated more, compared to a crash caused by a human driver.
They also judged that the crash caused by automation was more severe and less acceptable than one caused by a human, regardless of the severity of the crash.
Liu and his colleagues call this bias against automated systems blame attribution asymmetry. It indicates the tendency of people to overreact to shocks caused by automation, possibly due to the higher negative affect, or feelings and emotions, evoked by these shocks. Negative emotions such as anger can amplify attributions of legal responsibility and blame.
To change people's negative attitudes about semi-AVs, Liu maintains that "public communication campaigns are very necessary to transparently communicate accurate information, dispel public misconceptions, and provide opportunities to experience semi-AVs." .
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The news
We are more likely to blame a vehicle's autonomous system than its human driver when there is an accident
was originally published in
Xataka Science
by
Sergio Parra
.