Risk assessment algorithms have been correctly criticized for potential unfairness, and there is an active cottage industry trying to make repairs. In this paper, we adopt a framework from conformal prediction sets to remove unfairness from risk algorithms themselves and the covariates used for forecasting. From a sample of 300,000 offenders at their arraignments, we construct a confusion table and its derived measures of fairness that are effectively free any meaningful differences between Black and White offenders. We also produce fair forecasts for individual offenders coupled with valid probability guarantees that the forecasted outcome is the true outcome. We see our work as a demonstration of concept for application in a wide variety of criminal justice decisions. The procedures provided can be routinely implemented in jurisdictions with the usual criminal justice datasets used by administrators. The requisite procedures can be found in the scripting software R. However, whether stakeholders will accept our approach as a means to achieve risk assessment fairness is unknown. There also are legal issues that would need to be resolved although we offer a Pareto improvement.
翻译:风险评估算法被正确地批评为潜在的不公平,而且有一个活跃的家庭产业正在试图进行修复。在本文件中,我们从符合的预测组中采用一个框架,以消除风险算法本身和用于预测的共变体中的不公平现象。我们从30万罪犯的传讯中抽查了30万罪犯的样本中,构建了一个混乱表及其衍生的公正措施,有效地消除了黑人和白人罪犯之间的任何有意义的差异。我们还为个别罪犯提供了公平的预测,同时提供了预测的结果是真实结果的有效概率保证。我们认为,我们的工作是各种刑事司法决定中应用概念的示范。所提供的程序可以在各法域例行实施,使用管理人员通常使用的刑事司法数据集。必要的程序可以在编稿软件R中找到。然而,利益攸关方是否接受我们的方法,作为实现风险评估公平的一种手段是未知的。还有一些法律问题需要解决,尽管我们提出了改进方案。