AI 中文总结
该研究通过COMPAS数据集实证分析，发现算法公平性被高估，刑事司法中施加公平性约束不一定牺牲准确率，还揭示了种族偏见被模型放大的问题，为相关法律政策调整提供启示。
AI 中文摘要
对算法公平性的主流批评认为，提升公平性会降低预测准确率，给社会带来成本。我们通过对COMPAS数据集的实证分析挑战这一假设，作出两项贡献：第一，运用因果推断方法，我们证明COMPAS数据集不仅存在种族偏见，且经其训练的模型会放大这种偏见，广泛使用的模型不止复制现有偏见，还会加剧它，这削弱了“算法决策相比人类判断是中性改进”的假设，以及“仅反映既有人类偏见”的较弱主张；第二，我们重构了公平性-准确率的权衡关系，施加公平性约束不一定会在刑事司法中牺牲预测准确率，预测系统通过对预测内容与方式的隐含且常存在缺陷的规范性选择来实现风险等概念，权衡主张假设无约束模型的预测是最优基准，而公平性约束可纠正有偏结果变量引入的扭曲，本案中再逮捕数据会捕捉并放大系统性种族差异，因此在部分干预措施下，公平性不会带来政策辩论中所假定的成本，这种动态不仅限于刑事司法，还延伸至信贷、招聘、住房等领域，其中有偏结果变量会独立于代理选择加剧不平等，我们阐明了这对法律和政策应如何处理刑法领域公平性调整的启示。
英文摘要
A dominant critique of algorithmic fairness holds that increasing fairness reduces predictive accuracy, imposing a cost on society. We challenge that assumption by empirically analyzing the COMPAS dataset. We make two contributions. First, using causal inference methods, we show that racial bias is not only present in the COMPAS dataset but is also amplified by the models trained on it. Widely used models do more than replicate existing bias; they exacerbate it. This undercuts both the assumption that algorithmic decision-making offers a neutral improvement over human judgment and the weaker claim that it merely mirrors preexisting human bias. Second, we reframe the fairness-accuracy tradeoff. Applying fairness constraints does not necessarily cost predictive accuracy in criminal justice. Prediction systems operationalize concepts such as risk through implicit and often flawed normative choices about what to predict and how. The tradeoff claim assumes that the unconstrained model's prediction is an optimal baseline. Fairness constraints can instead correct distortions introduced by biased outcome variables: rearrest data, in this case, captures and magnifies systemic racial disparities. Under some interventions, therefore, fairness carries none of the cost presumed in policy debates. These dynamics extend beyond criminal justice to lending, hiring, and housing, where biased outcome variables reinforce inequality independently of proxy selection. We draw out what this implies for how law and policy should approach fairness adjustments in criminal law.
Comments48 pages, 1 figure, 12 tables. Published in the Indiana Law Journal
Journal refIndiana Law Journal 100 (2025) 1431-1478
