Understanding the Impact and Design of AI Teammate Etiquette

Abstract

Technical and practical advancements in Artificial Intelligence (AI) have led to AI teammates working alongside humans in an area known as human-agent teaming. While critical past research has shown the benefit to trust driven by the incorporation of interaction rules and structures (i.e. etiquette) in both AI tools and robotic teammates, research has yet to explicitly examine etiquette for digital AI teammates. Given the historic importance of trust within human-agent teams, the identification of etiquette's impact within said teams should be paramount. Thus, this study empirically evaluates the impact of AI teammate etiquette through a mixed-methods study that compares AI teammates that either adhere to or ignore traditional etiquette standards for machine systems. The quantitative results show that traditional etiquette adherence leads to greater trust, perceived performance of the AI, and perceived performance of the team as a whole. However, qualitative results reveal that not all traditional etiquette behaviors have universal appeal due to the presence of individual differences. This research provides the first empirical and explicit exploration of etiquette within human-agent teams, and the results of this study should be used to further design specific etiquette behaviors for AI teammates.