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

Takane Ueno, Yuto Sawa, Yeongdae Kim, Jacqueline Urakami, Hiroki Oura, Katie Seaborn

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

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.

Original languageEnglish
Title of host publicationCHI 2022 - Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems
PublisherAssociation for Computing Machinery
ISBN (Electronic)9781450391566
DOIs
Publication statusPublished - 27 Apr 2022
Event2022 CHI Conference on Human Factors in Computing Systems, CHI EA 2022 - Virtual, Online, United States
Duration: 30 Apr 20225 May 2022

Publication series

NameConference on Human Factors in Computing Systems - Proceedings

Conference

Conference2022 CHI Conference on Human Factors in Computing Systems, CHI EA 2022
Country/TerritoryUnited States
CityVirtual, Online
Period30/04/225/05/22

Keywords

  • Artificial intelligence
  • Automation
  • Decision aids
  • Robots
  • Scoping review
  • Trust

Fingerprint

Dive into the research topics of 'Trust in Human-AI Interaction: Scoping Out Models, Measures, and Methods'. Together they form a unique fingerprint.

Cite this