Why Did the Robot Cross the Road? A User Study of Explanation in Human-Robot Interaction

TL;DR

This paper presents a pilot user study on the effectiveness of different explanation types in human-robot interaction, emphasizing the importance of social science insights for improving explainable AI.

Contribution

It introduces a novel evaluation of contrastive, causal, and example explanations in HRI, integrating social science perspectives to enhance AI transparency.

Findings

Contrastive explanations improved user understanding.

Social science-informed explanations increased trust.

Different explanation types had varying effectiveness.

Abstract

This work documents a pilot user study evaluating the effectiveness of contrastive, causal and example explanations in supporting human understanding of AI in a hypothetical commonplace human robot interaction HRI scenario. In doing so, this work situates explainable AI XAI in the context of the social sciences and suggests that HRI explanations are improved when informed by the social sciences.