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Using Virtual Reality to Assess Ethical Decisions in Road Traffic Scenarios: Applicability of Value-of-Life-Based Models and Influences of Time Pressure

Overview of attention for article published in Frontiers in Behavioral Neuroscience, July 2017
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About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • Among the highest-scoring outputs from this source (#17 of 3,485)
  • High Attention Score compared to outputs of the same age (99th percentile)
  • High Attention Score compared to outputs of the same age and source (96th percentile)

Mentioned by

news
45 news outlets
blogs
9 blogs
twitter
113 X users
facebook
4 Facebook pages
wikipedia
6 Wikipedia pages
googleplus
6 Google+ users
reddit
3 Redditors
video
2 YouTube creators

Citations

dimensions_citation
74 Dimensions

Readers on

mendeley
153 Mendeley
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1 CiteULike
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Title
Using Virtual Reality to Assess Ethical Decisions in Road Traffic Scenarios: Applicability of Value-of-Life-Based Models and Influences of Time Pressure
Published in
Frontiers in Behavioral Neuroscience, July 2017
DOI 10.3389/fnbeh.2017.00122
Pubmed ID
Authors

Leon R. Sütfeld, Richard Gast, Peter König, Gordon Pipa

Abstract

Self-driving cars are posing a new challenge to our ethics. By using algorithms to make decisions in situations where harming humans is possible, probable, or even unavoidable, a self-driving car's ethical behavior comes pre-defined. Ad hoc decisions are made in milliseconds, but can be based on extensive research and debates. The same algorithms are also likely to be used in millions of cars at a time, increasing the impact of any inherent biases, and increasing the importance of getting it right. Previous research has shown that moral judgment and behavior are highly context-dependent, and comprehensive and nuanced models of the underlying cognitive processes are out of reach to date. Models of ethics for self-driving cars should thus aim to match human decisions made in the same context. We employed immersive virtual reality to assess ethical behavior in simulated road traffic scenarios, and used the collected data to train and evaluate a range of decision models. In the study, participants controlled a virtual car and had to choose which of two given obstacles they would sacrifice in order to spare the other. We randomly sampled obstacles from a variety of inanimate objects, animals and humans. Our model comparison shows that simple models based on one-dimensional value-of-life scales are suited to describe human ethical behavior in these situations. Furthermore, we examined the influence of severe time pressure on the decision-making process. We found that it decreases consistency in the decision patterns, thus providing an argument for algorithmic decision-making in road traffic. This study demonstrates the suitability of virtual reality for the assessment of ethical behavior in humans, delivering consistent results across subjects, while closely matching the experimental settings to the real world scenarios in question.

X Demographics

X Demographics

The data shown below were collected from the profiles of 113 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 153 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 153 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 30 20%
Researcher 26 17%
Student > Master 19 12%
Student > Bachelor 18 12%
Student > Doctoral Student 8 5%
Other 19 12%
Unknown 33 22%
Readers by discipline Count As %
Psychology 30 20%
Engineering 21 14%
Computer Science 14 9%
Business, Management and Accounting 10 7%
Neuroscience 8 5%
Other 24 16%
Unknown 46 30%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 467. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 09 July 2020.
All research outputs
#59,175
of 25,738,558 outputs
Outputs from Frontiers in Behavioral Neuroscience
#17
of 3,485 outputs
Outputs of similar age
#1,243
of 326,718 outputs
Outputs of similar age from Frontiers in Behavioral Neuroscience
#2
of 61 outputs
Altmetric has tracked 25,738,558 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 99th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 3,485 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 12.7. This one has done particularly well, scoring higher than 99% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 326,718 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 99% of its contemporaries.
We're also able to compare this research output to 61 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 96% of its contemporaries.