Trust in Human-AI Interaction: Scoping Out Models, Measures, and Methods
This work addresses the lack of standardized understanding and measurement of trust in AI systems, which is crucial for improving human-AI interaction, but it is incremental as it reviews existing literature without introducing new methods.
The paper conducted a scoping review to map models, measures, and methods used in studying trust in human-AI interaction, finding that most work is under-theorized and lacks standard approaches, with no concrete numerical results reported.
Trust has emerged as a key factor in people's interactions with AI-infused systems. Yet, little is known about what models of trust have been used and for what systems: robots, virtual characters, smart vehicles, decision aids, or others. Moreover, there is yet no known standard approach to measuring trust in AI. This scoping review maps out the state of affairs on trust in human-AI interaction (HAII) from the perspectives of models, measures, and methods. Findings suggest that trust is an important and multi-faceted topic of study within HAII contexts. However, most work is under-theorized and under-reported, generally not using established trust models and missing details about methods, especially Wizard of Oz. We offer several targets for systematic review work as well as a research agenda for combining the strengths and addressing the weaknesses of the current literature.