标题： 无偏见的证据：公平算法的新反事实测试 显示英文标题

标题： Evidence Without Injustice: A New Counterfactual Test for Fair Algorithms

摘要： 关于算法公平性的哲学文献日益增多，已经探讨了诸如平等机会和校准等统计标准，因果和反事实方法，以及结构性不公和累积性不公的作用。 然而，一个重要的方面却被忽视了：算法输出的证据价值本身是否依赖于结构性不公。 我们典型的例子对比了一个依赖历史犯罪数据的预测警务算法，与一个基于摄像头的系统，该系统记录正在进行的犯罪，两者都是为了指导警察部署。 在评估依据某条证据行动的道德可接受性时，我们必须不仅询问该证据在现实世界中是否有证明力，还要询问在没有相关不公的附近世界中，该证据是否仍然具有证明力。 预测警务算法未能通过这一测试，但基于摄像头的系统通过了这一测试。 当证据未能通过测试时，以惩罚性方式使用它是道德上有问题的，比通过测试的证据更为严重。 显示英文摘要

摘要： 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. Our paradigmatic pair of examples contrasts a predictive policing algorithm, which relies on historical crime data, with a camera-based system that records ongoing offenses, both 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.