Assessing the relationship between subjective trust, confidence measurements, and mouse trajectory characteristics in an online task

TL;DR

This study explores how mouse movement patterns relate to user trust and confidence in AI systems during an online task, aiming to develop a low-cost, non-invasive trust measurement method.

Contribution

It investigates the connection between mouse trajectory features and subjective trust and confidence ratings, providing insights into their potential as trust indicators.

Findings

Mouse trajectory aspects are influenced by trust and confidence ratings.

These measures alone may not fully explain trust levels.

The work suggests potential for low-cost trust assessment tools.

Abstract

Trust is essential for our interactions with others but also with artificial intelligence (AI) based systems. To understand whether a user trusts an AI, researchers need reliable measurement tools. However, currently discussed markers mostly rely on expensive and invasive sensors, like electroencephalograms, which may cause discomfort. The analysis of mouse trajectory has been suggested as a convenient tool for trust assessment. However, the relationship between trust, confidence and mouse trajectory is not yet fully understood. To provide more insights into this relationship, we asked participants (n = 146) to rate whether several tweets were offensive while an AI suggested its assessment. Our results reveal which aspects of the mouse trajectory are affected by the users subjective trust and confidence ratings; yet they indicate that these measures might not explain sufficiently the variance to be used on their own. This work examines a potential low-cost trust assessment in AI systems.