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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


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

Leon R Sütfeld et al. Front Behav Neurosci.


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.

Keywords: ethical decisions; modeling; moral judgment; self-driving cars; time pressure; value-of-life scale; virtual reality.


Figure 1
Figure 1
(Left) Overview of the experimental setting. (Middle) Timelines of the slow and fast conditions. (Right) Overview of all obstacles used. Colors indicate cluster assignments.
Figure 2
Figure 2
Model predictions. (Top) Slow condition, (Bottom) fast condition, (Left) pairing model, (Middle) obstacle model, (Right) cluster model. Colors indicate the probability of saving the row-obstacle (y-axis) and sacrificing the column-obstacle (x-axis). Pink, green, blue, and black bars indicate cluster assignments based on agglomerative clustering in the slow condition (see Figure 3). For means of comparability, the cluster model in the fast condition was fit with the semantic cluster assignments from the slow condition.
Figure 3
Figure 3
Dendrogram of bottom-up clustering, based on the observed frequencies with which each obstacle was spared (saved), for the slow and fast condition separately.
Figure 4
Figure 4
(Left) Value-of-life coefficients by condition. Pictograms and colors indicate the categories empty lane, inanimate objects, animals, humans, and groups of humans and animals (left to right). Starting lane coefficients depicted as gray bars. (Right) Relative frequency of “saving” the empty lane object, used as error rate estimates, for fast and slow condition separately.

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