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Perceived AI Fairness

Information SystemsReliability: alpha 0.894 sample items

About this scale

An 8-item measure of an employee or user's perception of fairness in AI-driven decisions, adapting Colquitt (2001) procedural and distributive justice items to algorithmic decision contexts (e.g., hiring, performance, lending). Captures whether outcomes are fair, procedures are bias-free, and explanations are adequate.

Sample item(s)

“The AI system makes fair decisions about employees.”
“The procedures used by the AI system are free of bias.”
“The AI system treats all employees consistently.”
“The AI system provides clear explanations for its decisions.”

Reliability

Reported internal consistency: alpha 0.89. As a rule of thumb, Cronbach’s α ≥ .70 is considered acceptable, ≥ .80 good, and ≥ .90 excellent for established measures.

Notes

Lee (2018) is also widely used; both adapt classic justice items to algorithmic decisions.

Source & citation

Robert, L. P., Pierce, C., Marquis, L., Kim, S., & Alahmad, R. (2020): Designing fair AI for managing employees in organizations: A review, critique, and design agenda. Human–Computer Interaction, Vol. 35, No. 5–6, pp. 545–575.

Robert et al. (2020): Designing fair AI for managing employees. HCI, 35(5–6), 545–575.

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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