October 3, 2025Open Access

The AI Fairness Myth: A Position Paper on Context-Aware Bias

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Fairness in AI cannot be universally defined, emphasizing the importance of context, and advocating for tailored approaches.
Traditional definitions often conflict, failing to meet multiple fairness constraints, leading to calls for corrective biases.
Framework proposed involves recognizing marginalized groups and applying intentional biases to enhance equality of opportunity.
Bridging mathematical rigor with ethical considerations, the approach aims to promote social justice in AI systems.

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Position paper explores the need for context-aware bias frameworks in AI fairness, highlighting justice and equality implications.

Nepomuceno et al. (2025) studied this question.

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