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To Trust or Distrust AI: A Questionnaire Validation Study

  • Nicolas Scharowski*
  • , Sebastian A.C. Perrig
  • , Nick Von Felten
  • , Lena Fanya Aeschbach
  • , Klaus Opwis
  • , Philipp Wintersberger
  • , Florian Brühlmann
  • *Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingsConference contributionpeer-review

7 Citations (Scopus)

Abstract

Despite the importance of trust in human-AI interactions, researchers must often rely on questionnaires adapted from other fields, which lack validation in the AI context. Motivated by the need for reliable and valid measures, we investigated the psychometric quality of the most commonly used trust questionnaire in the context of AI by Jian, Bisantz, and Drury (2000). In a pre-registered online experiment (N = 1485), participants observed interactions with both trustworthy and untrustworthy AI and rated their trust. Our results did not support the originally proposed single-factor structure for the questionnaire, but instead suggested a two-factor solution that distinguishes between trust and distrust. Based on our findings, we provide recommendations for future studies on how to use the questionnaire. Finally, we present arguments for considering trust and distrust as two distinct constructs, emphasizing the opportunities of considering and measuring both in human-AI interactions.
Original languageEnglish
Title of host publicationACMF AccT 2025 - Proceedings of the 2025 ACM Conference on Fairness, Accountability,and Transparency
PublisherAssociation for Computing Machinery, Inc
Pages361-374
Number of pages14
ISBN (Electronic)9798400714825
DOIs
Publication statusPublished - 23 Jun 2025
Event8th Annual ACM Conference on Fairness, Accountability, and Transparency, FAccT 2025 - Athens, Greece
Duration: 23 Jun 202526 Jun 2025

Publication series

NameACMF AccT 2025 - Proceedings of the 2025 ACM Conference on Fairness, Accountability,and Transparency

Conference

Conference8th Annual ACM Conference on Fairness, Accountability, and Transparency, FAccT 2025
Country/TerritoryGreece
CityAthens
Period23.06.202526.06.2025

Keywords

  • AI
  • Distrust
  • human-AI interaction
  • Measurement
  • Psychometrics
  • Questionnaires
  • Survey scale
  • Trust
  • Validation
  • XAI

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