Attachment and trust in artificial intelligence

By Omri Gillath, Ting Ai, Michael S. Branicky, S. Keshmiri, Robert B. Davison, R. Spaulding. Computers in Human Behavior, 2021

378 citations recorded by the source corpus.

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.

Subfields: Experimental and Cognitive Psychology, Social Psychology

View Paper: https://www.semanticscholar.org/paper/d7e73699049a73cf90c82a7b5aef589d84dbe794

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In Experimental and Cognitive Psychology

  1. Sylvia M. Chan-Olmsted
  2. Arun Ross
  3. Prabu David
  4. Jieun Shin
  5. Hyesun Choung

In Social Psychology

  1. Sylvia M. Chan-Olmsted
  2. Arun Ross
  3. Prabu David
  4. Jieun Shin
  5. Hyesun Choung

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