AI Trust Calibration
Information SystemsReliability: behavioural; latent-class indices3 sample items
About this scale
A multi-item measure of how well a user's trust in AI matches the AI's actual reliability — including over-trust (trust higher than reliability) and under-trust (trust lower than reliability).
Sample item(s)
“I usually trust AI outputs even when they may be wrong (over-trust risk)”
“I often double-check AI outputs even when they are usually correct (under-trust)”
“I adjust my trust in AI based on its track record”
Reliability
Reported internal consistency: behavioural; latent-class indices. As a rule of thumb, Cronbach’s α ≥ .70 is considered acceptable, ≥ .80 good, and ≥ .90 excellent for established measures.
Notes
Use alongside AI over-reliance and explainable-AI satisfaction.
Source & citation
Bansal, G., Wu, T., Zhou, J., Fok, R., Nushi, B., Kamar, E., Ribeiro, M. T., & Weld, D. (2021): Does the whole exceed its parts? The effect of AI explanations on complementary team performance. Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems, Article 81.
Bansal et al. (2021): AI explanations and team performance. CHI 2021, 81.
Source. Scale details are summarised from the publication cited above. Always consult the original source for the full item set, scoring, and permission to use.
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Need to check the reliability or psychometrics of this scale in your own data? Use the free DrAIStudio research tools:
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