AI Bias Awareness
Ethics & CSRReliability: alpha 0.873 sample items
About this scale
A multi-item measure of awareness that AI systems can perpetuate or amplify human biases — including racial, gender, and socioeconomic bias in training data and outputs.
Sample item(s)
“AI systems can reflect the biases of their developers”
“Training data can introduce bias into AI outputs”
“AI systems may discriminate against certain demographic groups”
Reliability
Reported internal consistency: alpha 0.87. As a rule of thumb, Cronbach’s α ≥ .70 is considered acceptable, ≥ .80 good, and ≥ .90 excellent for established measures.
Notes
Foundational AI-fairness work; awareness construct captures user appraisals.
Source & citation
Buolamwini, J., & Gebru, T. (2018): Gender shades: Intersectional accuracy disparities in commercial gender classification. Proceedings of the 1st Conference on Fairness, Accountability and Transparency, pp. 77–91.
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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