To Trust or Distrust AI: A Questionnaire Validation Study
Journal
Proceedings of the 2025 ACM Conference on Fairness, Accountability, and Transparency
Type
conference paper
Date Issued
2025-06-23
Author(s)
Scharowski, Nicolas
;
Perrig Sebastian Andrea Caesar
;
;
Wintersberger, Philipp
;
Opwis, Klaus
;
Brühlmann, Florian
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 (= 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.
Keywords
AI
XAI
Trust
Distrust
Measurement
Questionnaires
Survey scale
Validation
Psychometrics
human-AI interaction
Publisher
ACM
Subject(s)
Division(s)
File(s)![Thumbnail Image]()
Name
3715275.3732025.pdf
Size
1.47 MB
Format
Adobe PDF
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