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Do You Trust Me? Measuring People’s Perception of Being Trusted by AI in a Human–Agent Team

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

Abstract

Attaining mutual trust between humans and agents is considered pivotal in designing effective human-AI interaction methodologies. This research aims to develop and validate a questionnaire to measure people’s perception of AI agents’ trust in them. Two studies with 1373 participants were conducted. Study 1 focused on human-vehicle cooperation, using exploratory factor analysis (EFA) to develop the Perception of Being Trusted (PBT) scale, and the mediation analyses were performed to explore the mediation effect between the human’s propensity to trust and trust in the agent by PBT. Study 2 extended the scenario to human-AI cooperation, using confirmatory factor analysis (CFA) to validate the structure of PBT scale and path analysis were performed to explore the impact of PBT on behavioral intentions toward AI agents. Results confirm the PBT scale’s validity and predictive power, offering valuable insights into human psychological responses and promoting mutual trust in human-AI relationships. © 2025 Taylor & Francis Group, LLC
Original languageEnglish
Pages (from-to)13041-13058
JournalInternational Journal of Human-Computer Interaction
Volume41
Issue number20
Online published25 Mar 2025
DOIs
Publication statusPublished - 2025

Funding

This study was supported by the Aeronautical Science Foundation of China [2024Z074051003], the National Natural Science Foundation of China [NSFC, 72171015, 72021001, and 72071170], and the Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing.

Research Keywords

  • Trust
  • mutual trust
  • human-agent team
  • artificial intellignence
  • questionnaire development

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