论文标题
在偏见的嘈杂标签的情况下公平评估
Fairness Evaluation in Presence of Biased Noisy Labels
论文作者
论文摘要
风险评估工具在全国范围内广泛使用，以告知刑事司法系统内的决策。最近，已经大大关注这样的问题，即这种工具是否可能遭受种族偏见。在这种类型的评估中，一个基本问题是该模型的培训和评估是基于变量（逮捕），该变量可能代表了更中心利益（犯罪）的未观察结果的嘈杂版本。我们提出了一个灵敏度分析框架，用于评估跨组噪声的假设如何影响风险评估模型的预测偏差特性，以此作为重新犯的预测指标。我们对两个现实世界刑事司法数据集的实验结果表明，在观察到的标签中，即使是小偏见也可能会质疑基于嘈杂结果的分析的结论。
Risk assessment tools are widely used around the country to inform decision making within the criminal justice system. Recently, considerable attention has been devoted to the question of whether such tools may suffer from racial bias. In this type of assessment, a fundamental issue is that the training and evaluation of the model is based on a variable (arrest) that may represent a noisy version of an unobserved outcome of more central interest (offense). We propose a sensitivity analysis framework for assessing how assumptions on the noise across groups affect the predictive bias properties of the risk assessment model as a predictor of reoffense. Our experimental results on two real world criminal justice data sets demonstrate how even small biases in the observed labels may call into question the conclusions of an analysis based on the noisy outcome.
