ServiceNow Research

Investigating Trust Factors in Human-Robot Shared Control: Implicit Gender Bias Around Robot Voice


This paper explores the impact of warnings, audio feedback, and gender on human-robot trust in the context of autonomous driving and specifically shared robot control. We use pre-existing methods for the estimation and assessment of human-robot trust where trust was found to vary as a function of the quality of behavior of an autonomous driving controller. We extend these models and empirical methods to examine the impact of audio cues on trust, specifically studying the impacts of gender-specific audio cues on the elicitation of trust. Our study compares agents with and without human-voiced indicators of uncertainty and evaluates differences in trust with inferred and introspective methods. We find that a person’s trust in a robot can be influenced by verbal feedback from the robot agent. Specifically, people tend to lend more trust to agents whose voice is of the same gender as their own.

Conference on Computer and Robotic Vision (CRV)