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AI Ethical Concerns

Ethics & CSRReliability: alpha 0.89; subscale alphas 0.79–0.884 sample items

About this scale

A multidimensional measure of perceived ethical concerns about AI systems, including bias and discrimination, privacy, accountability, transparency, and societal harm. Validated across organizational and consumer samples and used widely in responsible-AI research.

Sample item(s)

“AI systems may discriminate against certain groups of people. (bias)”
“AI systems threaten people's privacy. (privacy)”
“It is unclear who is responsible when AI makes a wrong decision. (accountability)”
“AI may cause significant harm to society.”

Reliability

Reported internal consistency: alpha 0.89; subscale alphas 0.79–0.88. As a rule of thumb, Cronbach’s α ≥ .70 is considered acceptable, ≥ .80 good, and ≥ .90 excellent for established measures.

Notes

EU AI Act (2024) categories align with these dimensions; useful for ESG and responsible-AI scorecards.

Source & citation

Stahl, B. C., Antoniou, J., Bhalla, N., Brooks, L., Jansen, P., Lindqvist, B., Kirichenko, A., Marchal, S., Rodrigues, R., Santiago, N., Warso, Z., & Wright, D. (2023): A systematic review of artificial intelligence impact assessments. AI and Ethics, Vol. 3, pp. 175–197.

Stahl et al. (2023): Systematic review of AI impact assessments. AI and Ethics, 3, 175–197.

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:

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