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

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

研究成果: Conference contribution査読

抄録

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.

本文言語English
ホスト出版物のタイトルCHI 2022 - Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems
出版社Association for Computing Machinery
ISBN(電子版)9781450391566
DOI
出版ステータスPublished - 27 4月 2022
イベント2022 CHI Conference on Human Factors in Computing Systems, CHI EA 2022 - Virtual, Online, United States
継続期間: 30 4月 20225 5月 2022

出版物シリーズ

名前Conference on Human Factors in Computing Systems - Proceedings

Conference

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

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