Attachment and trust in artificial intelligence

被引:180
作者
Gillath, Omri [1 ]
Ai, Ting [1 ]
Branicky, Michael S. [2 ]
Keshmiri, Shawn [3 ]
Davison, Robert B. [4 ]
Spaulding, Ryan [5 ]
机构
[1] Univ Kansas, Dept Psychol, 1415 Jayhawk Blvd, Lawrence, KS 66045 USA
[2] Univ Kansas, Dept Elect Engn & Comp Sci, Lawrence, KS 66045 USA
[3] Univ Kansas, Dept Aerosp Engn, Lawrence, KS 66045 USA
[4] Colorado State Univ, Dept Management, Coll Business, Ft Collins, CO 80523 USA
[5] Univ Kansas, Sch Med, Dept Biostat & Data Sci, Kansas City, KS 66160 USA
关键词
Artificial intelligence; Attachment style; Close relationships; Trust; INTERPERSONAL-TRUST; ROBOTS; DYNAMICS; TEAMS; GOALS; NEED; FEAR;
D O I
10.1016/j.chb.2020.106607
中图分类号
B84 [心理学];
学科分类号
04 ; 0402 ;
摘要
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.
引用
收藏
页数:10
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