Attachment and trust in artificial intelligence. (February 2021)

• Record Type: Journal Article
• Authors: Gillath, Omri; Ai, Ting; Branicky, Michael S.; Keshmiri, Shawn; Davison, Robert B.; Spaulding, Ryan
• Is Part Of: Computers in human behavior. Volume 115(2021)
• Publication Date: 2021-02
• Subjects: Artificial intelligence -- Attachment style -- Close relationships -- Trust; Interactive computer systems -- Periodicals; Man-machine systems -- Periodicals; 004.019
• DOI: 10.1016/j.chb.2020.106607
• Languages: English
• ISSNs: 0747-5632

Abstract

Abstract: Lack of trust is one of the main obstacles standing in the way of taking full advantage of the benefits artificial intelligence (AI) has to offer. Most research on trust in AI focuses on cognitive ways to boost trust. Here, instead, we focus on boosting trust in AI via affective means. Specifically, we tested and found associations between one's attachment style—an individual difference representing the way people feel, think, and behave in relationships—and trust in AI. In Study 1 we found that attachment anxiety predicted less trust. In Study 2, we found that enhancing attachment anxiety reduced trust, whereas enhancing attachment security increased trust in AI. In Study 3, we found that exposure to attachment security cues (but not positive affect cues) resulted in increased trust as compared with exposure to neutral cues. Overall, our findings demonstrate an association between attachment security and trust in AI, and support the ability to increase trust in AI via attachment security priming. Highlights: Attachment style—how people feel, think, and behave in relationships predicts trust. Attachment anxiety predicted less trust in Artificial Intelligence. Enhancing attachment anxiety reduced trust in AI. Enhancing attachment security increased trust in AI. Exposure to positive affect cues did not have the same effect on trust in AI.