刑事司法风险评估中的不可靠类别分配
Criminal Justice Risk Assessments with Unreliable Class Assignments
- University of Pennsylvania（宾夕法尼亚大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对刑事司法风险评估中类别分配不可靠的问题，本文提出使用Mondrian共形预测集来提高分类可靠性并提供有效的预测不确定性估计，并通过缓刑罪犯数据加以验证。
AI中文摘要：
定量风险评估已被用于帮助刑事司法决策数十年。一组预测变量和一个分类响应变量被用来训练一个统计分类器。训练好的分类器可用于为需要预测的新未标记案例计算风险评分，每个响应变量类别对应一个风险评分。按照贝叶斯分类器的精神，风险评分最大的类别通常成为该案例的预测结果标签。最大和次大的风险评分可能非常相似或非常不同。然而，当它们非常接近时，结果类别实质上是通过抛硬币来分配的；这种分类是不可靠的。预测错误更可能发生，公平性问题也可能出现。本文展示了当分类器犹豫不决时，Mondrian共形预测集如何提高分类可靠性，并提供预测不确定性的有效估计。对大量缓刑罪犯数据集的风险预测被用来说明这些问题。
英文摘要：
Quantitative risk assessments have been used for decades to help inform criminal justice decisions. A set of predictors and a categorical response variable are used to train a statistical classifier. The trained classifier can be employed to compute risk scores for a new, unlabeled case for which a forecast is needed, one risk score for each response variable class. In the spirit of Bayes classifiers, the class with the largest risk score conventionally becomes the forecasted outcome label for that case. The largest and next largest risk scores can be very similar or very different. However, when they are much the same, the outcome class is essentially being assigned by a coin flip; the classification is unreliable. Forecasting error becomes more likely, and concerns about fairness can arise. This paper shows how Mondrian conformal prediction sets can increase classification reliability when the classifier is indecisive and provide valid estimates of forecast uncertainty. Risk forecasts for a large dataset of offenders on probation are used to illustrate the issues.
