论文标题
使用保形预测集改善刑事司法算法风险评估的公平性
Improving Fairness in Criminal Justice Algorithmic Risk Assessments Using Conformal Prediction Sets
论文作者
论文摘要
风险评估算法已经正确批评了潜在的不公平性，并且有一个活跃的家庭手工业试图进行维修。在本文中，我们采用了共形预测集的框架，以消除风险算法本身和用于预测的协变量中的不公平性。从提审的300,000名罪犯的样本中，我们构建了一个混乱的表格及其派生的公平度量，这些措施实际上可以释放黑人和白人罪犯之间的任何有意义的差异。我们还为个人罪犯提供公平的预测，并确保预测结果是真正的结果。我们认为我们的工作是在各种刑事司法决定中应用的概念的演示。提供的程序可以与管理人员使用的通常的刑事司法数据集在司法管辖区进行常规实施。必要的程序可以在脚本软件R中找到。但是，利益相关者是否会接受我们的方法作为实现风险评估公平的手段是未知的。尽管我们提供了帕累托的改进，但也需要解决一些法律问题。
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.
