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<p>The pursuit of algorithmic fairness is often framed as a search for universal, mathematically sound principles. Yet in high-stakes domains, e.g., criminal justice, finance, and healthcare, these abstractions collide with distinct ethical, legal, and historical realities. While prior research recognizes fairness as contextual, its systematic operationalization remains unresolved. This paper asks: Do definitions and priorities of fairness systematically differ across these domains, and if so, how? Through comparative analysis of international case studies and fairness literature, we identify unique “fairness signatures” shaped by each domain’s core values, harm profiles, legal mandates, and power dynamics. Our research shows that criminal justice prioritizes procedural safeguards and false positive minimization to uphold due process; finance emphasizes explainability and anti-discrimination compliance to ensure equal opportunity; healthcare balances individualized care with equitable outcomes rooted in bioethics. We show that impossibility theorems reflect not technical limits but irreducible value conflicts. To address this, we propose the Context-Aware Fairness Framework (CAFF), a structured, deliberative methodology for selecting fairness criteria that are ethically grounded, legally viable, and contextually appropriate.</p>

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Algorithmic fairness in context: liberty, opportunity, and well-being as ethical anchors

  • 期刊论文

  • 发表日期：

    2026年1月14日

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  • Arif Perdana,
  • Phoebe Li,
  • Grace Wangge

摘要

The pursuit of algorithmic fairness is often framed as a search for universal, mathematically sound principles. Yet in high-stakes domains, e.g., criminal justice, finance, and healthcare, these abstractions collide with distinct ethical, legal, and historical realities. While prior research recognizes fairness as contextual, its systematic operationalization remains unresolved. This paper asks: Do definitions and priorities of fairness systematically differ across these domains, and if so, how? Through comparative analysis of international case studies and fairness literature, we identify unique “fairness signatures” shaped by each domain’s core values, harm profiles, legal mandates, and power dynamics. Our research shows that criminal justice prioritizes procedural safeguards and false positive minimization to uphold due process; finance emphasizes explainability and anti-discrimination compliance to ensure equal opportunity; healthcare balances individualized care with equitable outcomes rooted in bioethics. We show that impossibility theorems reflect not technical limits but irreducible value conflicts. To address this, we propose the Context-Aware Fairness Framework (CAFF), a structured, deliberative methodology for selecting fairness criteria that are ethically grounded, legally viable, and contextually appropriate.