October 2, 2025Open Access

Algorithmic Fairness: Not a Purely Technical but Socio-Technical Property

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Algorithmic fairness cannot be solely defined by technical metrics, emphasizing its socio-technical nature.
Current fairness measures often overlook intersectionality and lack consensus, limiting their practical application.
Conceptual analysis reveals contradictions between accuracy and fairness, questioning existing standards.
Three principles are proposed as essential for designing effective fairness measures within AI systems.

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This analysis examines algorithmic fairness in AI and ML systems, highlighting misconceptions and implications for equitable outcomes.

Bian et al. (2025) studied this question.

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