提出反事实公平性测试，判断算法证据是否受结构性不公影响。
Evidence Without Injustice: A New Counterfactual Test for Fair Algorithms
- 用反事实世界检验算法证据在无不公情况下的有效性
- 预测警务算法因依赖历史数据而未通过测试，监控系统则通过
- 适用于评估算法决策的道德可接受性，尤其关注系统性偏见
算法公平性研究常关注等几率、校准等统计标准，以及因果与反事实方法，但忽视了一个关键问题：算法输出的证据价值是否依赖于结构性不公。本文对比了基于历史犯罪数据的预测警务算法与记录实时犯罪的摄像头系统，两者均用于指导警力部署。评估使用某项证据的道德合理性时，不仅要问其在现实世界中是否具有证明力，还要考察在没有相关不公的邻近世界中是否依然有效。结果表明，预测警务算法未能通过该测试，而摄像头系统通过了。当证据不通过测试时，将其用于惩罚性决策在道德上更具问题性，远高于通过测试的证据。
原文摘要 · Abstract (English)
The growing philosophical literature on algorithmic fairness has examined statistical criteria such as equalized odds and calibration, causal and counterfactual approaches, and the role of structural and compounding injustices. Yet an important dimension has been overlooked: whether the evidential value of an algorithmic output itself depends on structural injustice. We contrast a predictive policing algorithm, which relies on historical crime data, with a camera-based system that records ongoing offenses, where both are designed to guide police deployment. In evaluating the moral acceptability of acting on a piece of evidence, we must ask not only whether the evidence is probative in the actual world, but also whether it would remain probative in nearby worlds without the relevant injustices. The predictive policing algorithm fails this test, but the camera-based system passes it. When evidence fails the test, it is morally problematic to use it punitively, more so than evidence that passes the test.
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