AI Accountability Attribution
Ethics & CSRReliability: alpha 0.843 sample items
About this scale
A multi-item measure of how users attribute responsibility for AI outcomes — to the AI itself, the developer, the user, or the deploying organization. Captures the moral responsibility-gap problem.
Sample item(s)
“When AI makes a wrong decision, the developer should be held accountable”
“When AI makes a wrong decision, the organization deploying it should be held accountable”
“Users should be held accountable for the consequences of using AI tools”
Reliability
Reported internal consistency: alpha 0.84. As a rule of thumb, Cronbach’s α ≥ .70 is considered acceptable, ≥ .80 good, and ≥ .90 excellent for established measures.
Notes
Pair with AI ethical concerns for full responsibility-and-ethics modelling.
Source & citation
Constantinescu, M., Voinea, C., Uszkai, R., & Vică, C. (2022): Understanding responsibility in responsible AI: Dianoetic virtues and the hard problem of context. Ethics and Information Technology, Vol. 23, pp. 803–814.
Constantinescu et al. (2022): Responsibility in responsible AI. EIT, 23, 803–814.
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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