Explainable AI (XAI) Satisfaction (Hoffman et al.)
Information SystemsReliability: alpha 0.884 sample items
About this scale
An 8-item measure of user satisfaction with the explanations provided by an AI system, capturing understandability, sufficiency, completeness, satisfaction, and trust-in-explanation. Widely used in XAI evaluations of decision-aid AI and recommender systems.
Sample item(s)
“From the explanation, I understand how the AI works.”
“The explanation is sufficiently detailed.”
“The explanation is satisfying.”
“The explanation lets me know how trustworthy the AI is.”
Reliability
Reported internal consistency: alpha 0.88. As a rule of thumb, Cronbach’s α ≥ .70 is considered acceptable, ≥ .80 good, and ≥ .90 excellent for established measures.
Notes
Most adopted XAI metric set; freely available with attribution.
Source & citation
Hoffman, R. R., Mueller, S. T., Klein, G., & Litman, J. (2023): Measures for explainable AI: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-AI performance. Frontiers in Computer Science, Vol. 5, Article 1096257.
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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