提出更实用的公平性优化方法，平衡算法公正与预测准确
Alternative Fairness and Accuracy Optimization in Criminal Justice
- 用加权误差损失替代严格群体公平，放宽假阴性率差异容忍度
- 在保持公平性前提下提升预测准确率，明确误差代价的伦理选择
- 适合政策制定者和司法系统使用，提供可落地的部署框架
算法公平性研究快速发展，但在刑事司法领域关键概念仍不明确。本文梳理了群体、个体与过程公平的定义，并分析其冲突条件。提出对标准群体公平的简化改进：不追求各保护群体间完全平等，而是最小化加权误差损失，同时将假阴性率差异控制在小容忍范围内。该方法更易求解，可提升预测准确性，并凸显误差成本的伦理权衡。将该提议置于三类批评之下：数据偏差与不完整、隐性正向行动、子群约束爆炸。最后提出面向公共决策系统的实践框架，包含三大支柱：基于需求的决策、透明与问责、精准定义与解决方案。这些要素将技术设计与合法性连接，为使用风险评估工具的机构提供可操作指导。
原文摘要 · Abstract (English)
Algorithmic fairness has grown rapidly as a research area, yet key concepts remain unsettled, especially in criminal justice. We review group, individual, and process fairness and map the conditions under which they conflict. We then develop a simple modification to standard group fairness. Rather than exact parity across protected groups, we minimize a weighted error loss while keeping differences in false negative rates within a small tolerance. This makes solutions easier to find, can raise predictive accuracy, and surfaces the ethical choice of error costs. We situate this proposal within three classes of critique: biased and incomplete data, latent affirmative action, and the explosion of subgroup constraints. Finally, we offer a practical framework for deployment in public decision systems built on three pillars: need-based decisions, Transparency and accountability, and narrowly tailored definitions and solutions. Together, these elements link technical design to legitimacy and provide actionable guidance for agencies that use risk assessment and related tools.
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