English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

The development of self-driving vehicles is proceeding rapidly and with significant investment of resources. However, a full-scale deployment is not imminent. Among the challenges self-driving vehicles are facing, they will have to navigate complex ethical challenges. The algorithms governing their behavior will have to decide how to steer them in situations where accidents cannot be avoided. In some of these situations they will have to decide which of several potential parties to injure in the process. We investigate the preferences of Swiss customers for this decision by forcing a selection between simplified scenarios where a given number of car passengers or a given number of pedestrians will be killed in the accident. Both passengers and pedestrians can be adults or children. The passengers are explicitly identified as the respondent themselves and their family. While children are implicitly valued higher than adults, Swiss customers value passengers and pedestrians implicitly roughly equally, and assign increasingly higher marginal values to additional people, both passengers and pedestrians. These results seem to partially contradict similar studies conducted in other countries and recent statements by automotive companies, potentially indicating the need to adapt both corporate communications and steering algorithms in different geographies.

Customer Preferences and Implicit Tradeoffs in Accident Scenarios for Self-Driving Vehicle Algorithms