Effects of Explanation Specificity on Passengers in Autonomous Driving

TL;DR

This study examines how the level of detail in natural language explanations affects passenger perceptions and control desires in autonomous driving, using a simulated environment with different explanation types.

Contribution

It introduces a rule-based extension to an existing explainer algorithm and evaluates the impact of explanation specificity on passenger attitudes in a driving simulation.

Findings

Both explanation types increased perceived safety and reduced anxiety.

Specific explanations increased passengers' desire to take control.

Abstract explanations did not influence control desire.

Abstract

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.