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<p>Artificial intelligence systems increasingly influence critical societal decisions, from healthcare resource distribution to criminal justice evaluations. While John Rawls’ theory of justice, especially his “veil of ignorance,” has become a prominent framework for promoting algorithmic fairness, this paper reveals a fundamental paradox in its use: unchanging principles of justice, when applied in evolving AI systems, can sustain or worsen existing inequalities. We demonstrate how algorithmic systems that meet initial Rawlsian fairness standards can create growing disparities through feedback loops and systemic biases. Our healthcare case study shows how seemingly neutral resource allocation algorithms can systematically harm vulnerable groups even when they meet formal fairness criteria.</p>

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Beyond the Veil: why Rawlsian Fairness is Too Blind

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年3月12日

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  • Dwayne Woods

摘要

Artificial intelligence systems increasingly influence critical societal decisions, from healthcare resource distribution to criminal justice evaluations. While John Rawls’ theory of justice, especially his “veil of ignorance,” has become a prominent framework for promoting algorithmic fairness, this paper reveals a fundamental paradox in its use: unchanging principles of justice, when applied in evolving AI systems, can sustain or worsen existing inequalities. We demonstrate how algorithmic systems that meet initial Rawlsian fairness standards can create growing disparities through feedback loops and systemic biases. Our healthcare case study shows how seemingly neutral resource allocation algorithms can systematically harm vulnerable groups even when they meet formal fairness criteria.