Algorithmic Fairness Perceptions Scale (AFPS)
AI / HR TechReliability: alpha = 0.893 sample items
About this scale
A 12-item scale capturing employees' perceptions of fairness in algorithmic HR decisions across procedural, distributive, and interactional facets. Designed for gig and platform contexts.
Sample item(s)
“The algorithm used to evaluate my work treats everyone equally.”
“I can understand how the algorithm reached its decision about my performance.”
“I trust the data the algorithm uses to evaluate me.”
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
Useful for studies of AI hiring, gig labor, and digital performance management. Sub-scales: procedural (4), distributive (4), interactional (4).
Source & citation
Newman, D. T., Fast, N. J., & Harmon, D. J. (2020). When eliminating bias isn't fair: Algorithmic reductionism and procedural justice in human resource decisions. Organizational Behavior and Human Decision Processes, 160, 149-167.
Newman et al. (2020): Algorithmic reductionism and procedural justice. OBHDP, 160, 149-167.
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.
Use it in your research
Need to check the reliability or psychometrics of this scale in your own data? Use the free DrAIStudio research tools: