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<p>Algorithmic fairness research has largely emphasized model-level metrics, despite evidence that algorithmic systems behave differently once deployed in real-world, complex health systems. This paper reframes fairness as a system-level property and argues that integrating complexity science, implementation research, and sociotechnical perspectives is essential to addressing a central unresolved question: whether fairness-informed algorithms can sustainably narrow health inequalities over time.</p>

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Algorithmic fairness: a complexity science perspective for public health research and practice

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年8月5日

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  • Tibor V. Varga

摘要

Algorithmic fairness research has largely emphasized model-level metrics, despite evidence that algorithmic systems behave differently once deployed in real-world, complex health systems. This paper reframes fairness as a system-level property and argues that integrating complexity science, implementation research, and sociotechnical perspectives is essential to addressing a central unresolved question: whether fairness-informed algorithms can sustainably narrow health inequalities over time.