Internalized Algorithmic Bias
Information SystemsReliability: alpha 0.853 sample items
About this scale
A multi-item measure of the degree to which users internalize biased AI outputs — accepting and acting on them despite awareness of bias.
Sample item(s)
“I have used AI outputs even when I suspected they might be biased”
“I sometimes accept AI suggestions that I know reflect stereotypes”
“Even when I see bias in AI, I find it hard to reject the output”
Reliability
Reported internal consistency: alpha 0.85. As a rule of thumb, Cronbach’s α ≥ .70 is considered acceptable, ≥ .80 good, and ≥ .90 excellent for established measures.
Notes
Pair with AI bias awareness.
Source & citation
Bertrand, A., Belloum, R., Eagan, J. R., & Maxwell, W. (2022): How cognitive biases affect XAI-assisted decision-making: A systematic review. Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 78–91.
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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