论文标题
一种完全贝叶斯的逻辑回归跟踪算法，用于减轻不同的错误分类
A Fully Bayesian, Logistic Regression Tracking Algorithm for Mitigating Disparate Misclassification
论文作者
论文摘要
我们开发了一种完全贝叶斯的逻辑跟踪算法，目的是提供分类结果，这些分类结果均匀地应用于具有不同敏感变量值的个体。在这里，我们考虑以不同敏感变量组之间错误预测率的差异形式的偏差。鉴于该方法是完全贝叶斯的，因此非常适合组参数或逻辑回归系数为动态数量的情况。与其他人相比，我们在模拟数据集和著名的ProPublica Compas数据集上说明了我们的方法。
We develop a fully Bayesian, logistic tracking algorithm with the purpose of providing classification results that are unbiased when applied uniformly to individuals with differing sensitive variable values. Here, we consider bias in the form of differences in false prediction rates between the different sensitive variable groups. Given that the method is fully Bayesian, it is well suited for situations where group parameters or logistic regression coefficients are dynamic quantities. We illustrate our method, in comparison to others, on both simulated datasets and the well-known ProPublica COMPAS dataset.
