Attribution of blame of crash causation across varying levels of vehicle automation

Bennett, Joanne M.; Challinor, Kirsten L.; Modesto, Oscar; Prabhakharan, Prasannah

doi:10.1016/j.ssci.2020.104968

Cited by 45 publications

(18 citation statements)

References 20 publications

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“…This has, and will, lead to new situations where the developers of such intelligent machines are not able to fully predict their machines behavior and thus mistakes. The current study contributes to the existing literature ( Malle et al, 2015 ; Awad et al., 2020 ; Bennett et al., 2020 ; Hidalgo et al, 2021 ) seeking to understand how situations of failure shape the public's attitude toward machines. Exploring how this is likely to unfold is a crucial step forward toward realizing the potential benefit of this technology.…”

Section: Discussionmentioning

confidence: 90%

Blaming automated vehicles in difficult situations

2021

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Summary Automated vehicles (AVs) have made huge strides toward large-scale deployment. Despite this progress, AVs continue to make mistakes, some resulting in death. Although some mistakes are avoidable, others are hard to avoid even by highly skilled drivers. As these mistakes continue to shape attitudes toward AVs, we need to understand whether people differentiate between them. We ask the following two questions. When an AV makes a mistake, does the perceived difficulty or novelty of the situation predict blame attributed to it? How does that blame attribution compare to a human driving a car? Through two studies, we find that the amount of blame people attribute to AVs and human drivers is sensitive to situation difficulty. However, while some situations could be more difficult for AVs and others for human drivers, people blamed AVs more, regardless. Our results provide novel insights in understanding psychological barriers influencing the public's view of AVs.

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Section: Discussionmentioning

confidence: 90%

Blaming automated vehicles in difficult situations

2021

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“…This has, and will, lead to new situations where the developers of such intelligent machines are not able to fully predict their machines behavior and thus mistakes. The current study contributes to the existing literature (Malle et al, 2015;Awad et al, 2020;Bennett et al, 2020;Hidalgo et al, 2021) seeking to understand how situations of failure shape the public's attitude toward machines. Exploring how this is likely to unfold is a crucial step forward toward realizing the potential benefit of this technology.…”

Section: Discussionmentioning

confidence: 90%

Blaming Automated Vehicles in Difficult Situations

2020

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Automated vehicles (AVs) have made huge strides toward large-scale deployment. Despite this progress, AVs continue to make mistakes, some resulting in death. Although some mistakes are avoidable, others are hard to avoid even by highly skilled drivers. As these mistakes continue to shape attitudes toward AVs, we need to understand whether people differentiate between them. We ask the following two questions. When an AV makes a mistake, does the perceived difficulty or novelty of the situation predict blame attributed to it? How does that blame attribution compare to a human driving a car? Through two studies, we find that the amount of blame people attribute to AVs and human drivers is sensitive to situation difficulty. However, while some situations could be more difficult for AVs and others for human drivers, people blamed AVs more, regardless. Our results provide novel insights in understanding psychological barriers influencing the public's view of AVs.

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“…In this work we investigate public perception of liability. We conducted a survey to assess how people apportion liability between driver, manufacturer and infrastructure owner, in a similar manner to Bennett et al 2020 [15]. We also assess how safe people consider AVs to be.…”

Section: Safety and Liabilitymentioning

confidence: 99%

Public perception on the responsibility for incidents involving autonomous vehicles

Rutte¹,

Atkins²,

Otterloo³

2021

Com Soc Res J

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This study investigates the public perception of the blame for incidents where existing autonomous vehicles (AVs) make poor decisions when faced with challenging road infrastructure. We particularly focus on whether the road owner may be considered to be at blame for the incident. We found that respondents overwhelmingly blamed the vehicle for the incident, with the second most common liable party being the driver. The road owner received only a small part of the blame. Many respondents felt that the AV 'should just work'. We conclude that the public feels that AVs should be programmed to handle road infrastructure in its current state, rather than expecting infrastructure to be adapted for the needs of autonomous vehicles. Respondents were also asked how safe they consider AVs to be. Very few respondents considered AVs to be safe currently, although that may be because they had just watched videos of incidents involving AVs. Their level of distrust may be lower than reported, because despite saying that they feel AVs to be unsafe, the majority would use AV features in at least some circumstances.

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Attribution of blame of crash causation across varying levels of vehicle automation

Cited by 45 publications

References 20 publications

Blaming automated vehicles in difficult situations

Blaming automated vehicles in difficult situations

Blaming Automated Vehicles in Difficult Situations

Public perception on the responsibility for incidents involving autonomous vehicles

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