Communicating trust-based beliefs and decisions in human-AI teams: The impact of textual summary of changes of the artificial agent's mental model on the human teammate's trust in the agent and overall satisfaction

Abstract

As artificial intelligence (AI) is increasingly integrated into decision-making processes, effective collaboration between humans and AI becomes crucial. This study investigates how textual summaries of changes of artificial agent's mental model affect human trust and overall satisfaction. Using a between-groups experimental design in an Urban Search and Rescue scenario, 56 participants were randomly assigned to either receive or not receive these summaries. Trust and satisfaction were measured through established scales and objective metrics, including the number of actions the human and AI performed together. Results show that providing textual summaries significantly increased both human trust in the AI agent and overall satisfaction. While the Task success rate improved with additional communication, other performance metrics showed no significant differences. This research contributes to understanding effective communication strategies in human-AI teams, highlighting the importance of transparency and justifications from artificial agents. These findings can help the design of collaborative AI systems, enhancing trust and satisfaction in human-AI partnerships.