Algorithms are increasingly used in different domains of public policy. They help humans to profile unemployed, support administrations to detect tax fraud and give recidivism risk scores that judges or criminal justice managers take into account when they make bail decisions. In recent years, critics have increasingly pointed to ethical challenges of these tools and emphasized problems of discrimination, opaqueness or accountability, and computer scientists have proposed technical solutions to these issues. In contrast to these important debates, the literature on how these tools are implemented in the actual everyday decision-making process has remained cursory. This is problematic because the consequences of ADM systems are at least as dependent on the implementation in an actual decision-making context as on their technical features. In this study, we show how the introduction of risk assessment tools in the criminal justice sector on the local level in the USA has deeply transformed the decision-making process. We argue that this is mainly due to the fact that the evidence generated by the algorithm introduces a notion of statistical prediction to a situation which was dominated by fundamental uncertainty about the outcome before. While this expectation is supported by the case study evidence, the possibility to shift blame to the algorithm does seem much less important to the criminal justice actors.

在公共政策的不同领域中越来越多地使用算法。它们帮助人们描述失业情况，支持主管部门检测税收欺诈并提供累犯风险评分，法官或刑事司法经理在做出保释决定时会考虑这些评分。近年来，批评家越来越多地指出了这些工具的道德挑战，并强调了歧视，不透明或问责制的问题，并且计算机科学家已针对这些问题提出了技术解决方案。与这些重要的辩论相反，有关如何在实际的日常决策过程中实现这些工具的文献仍然很模糊。这是有问题的，因为ADM系统的后果至少取决于实际决策环境中的实现及其技术特征。在这项研究中，我们展示了在美国地方一级的刑事司法部门中引入风险评估工具如何深刻地改变了决策过程。我们认为这主要是由于以下事实：该算法生成的证据将统计预测的概念引入了一种情况，该情况以前由对结果的基本不确定性所支配。尽管这种期望得到了案例研究证据的支持，但对于刑事司法行为者而言，将责任转移到算法上的可能性似乎并不重要。我们认为这主要是由于以下事实：该算法生成的证据将统计预测的概念引入了一种情况，该情况以前由对结果的基本不确定性所支配。尽管这种期望得到了案例研究证据的支持，但对于刑事司法行为者而言，将责任转移到算法上的可能性似乎并不重要。我们认为这主要是由于以下事实：该算法生成的证据将统计预测的概念引入了一种情况，该情况以前由对结果的基本不确定性所支配。尽管这种期望得到了案例研究证据的支持，但对于刑事司法行为者而言，将责任转移到算法上的可能性似乎并不重要。