Effects of Explanation Specificity on Passengers in Autonomous Driving
Daniel Omeiza Raunak Bhattacharyya Nick Hawes Marina Jirotka Lars Kunze
The nature of explanations provided by an explainable AI algorithm has been a
topic of interest in the explainable AI and human-computer interaction
community. In this paper, we investigate the effects of natural language
explanations' specificity on passengers in autonomous driving. We extended an
existing data-driven tree-based explainer algorithm by adding a rule-based
option for explanation generation. We generated auditory natural language
explanations with different levels of specificity (abstract and specific) and
tested these explanations in a within-subject user study (N=39) using an
immersive physical driving simulation setup. Our results showed that both
abstract and specific explanations had similar positive effects on passengers'
perceived safety and the feeling of anxiety. However, the specific explanations
influenced the desire of passengers to takeover driving control from the
autonomous vehicle (AV), while the abstract explanations did not. We conclude
that natural language auditory explanations are useful for passengers in
autonomous driving, and their specificity levels could influence how much
in-vehicle participants would wish to be in control of the driving activity.