论文标题
多类分类器中的不同条件预测
Disparate Conditional Prediction in Multiclass Classifiers
论文作者
论文摘要
我们提出了在多类均衡赔率下审核多类分类器公平性的方法，当分类器不完全公平时，估计与均衡赔率的偏差。我们将多类分类器推广到不同条件预测（DCP）的度量，最初由Sabato＆Yom-TOV（2020）建议用于二进制分类器。 DCP被定义为分类器对有条件预测概率预测的人群的比例，与最接近的公共基线不同。我们提供了新的局部优化方法来估计多类DCPUNDER两种不同的方案，其中已知每个受保护的亚种群的条件混淆矩阵，并且无法估计这些矩阵，例如，因为分类器是无法访问的，或者是因为无法获得良好的个人水平数据。这些方法可用于检测可能不公平地处理大量人群的分类器。实验证明了方法的准确性。代码可在https://github.com/sivansabato/ dcpmulticlass上提供。
We propose methods for auditing multiclass classifiers for fairness under multiclass equalized odds,by estimating the deviation from equalized odds when the classifier is not completely fair. We generalize to multiclass classifiers the measure of Disparate Conditional Prediction (DCP), originally suggested by Sabato & Yom-Tov (2020) for binary classifiers. DCP is defined as the fraction of the population for which the classifier predicts with conditional prediction probabilities that differ from the closest common baseline. We provide new local-optimization methods for estimating the multiclass DCPunder two different regimes,one in which the conditional confusion matrices for each protected sub-population are known, and one in which these cannot be estimated, for instance, because the classifier is inaccessible or because good-quality individual-level data is not available. These methods can be used to detect classifiers that likely treat a significant fraction of the population unfairly. Experiments demonstrate the accuracy of the methods. Code is provided at https://github.com/sivansabato/ DCPmulticlass.
