Trust in Human-AI Interaction: Scoping Out Models, Measures, and Methods

TL;DR

This paper reviews the current state of trust in human-AI interaction, highlighting the lack of standard models and measurement approaches, and proposes a research agenda to improve understanding and assessment of trust in AI systems.

Contribution

It provides a comprehensive mapping of trust models, measures, and methods used in human-AI interaction research, identifying gaps and suggesting directions for future systematic review and study.

Findings

Trust is multi-faceted and important in HAII.

Most research lacks use of established trust models.

Methods like Wizard of Oz are under-reported.

Abstract

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.