公平评估中的不确定性：尽管波动，但仍保持稳定的结论
Uncertainty in Fairness Assessment: Maintaining Stable Conclusions Despite Fluctuations
摘要：
最近的几项工作鼓励使用贝叶斯框架时，评估性能和公平性指标的分类算法在监督设置。我们提出的不确定性事项（UM）的框架，概括了一个贝塔-二项式的方法来获得任何标准组合的后验分布，允许稳定的性能评估在偏差感知setting.We建议建模的混淆矩阵的每个人口群体使用更新通过贝叶斯过程的多项分布。我们将UM扩展为适用于流行的K折交叉验证程序。实验突出了UM在信息量和稳定性方面优于经典评估框架的优势。
英文摘要：
Several recent works encourage the use of a Bayesian framework when assessing performance and fairness metrics of a classification algorithm in a supervised setting. We propose the Uncertainty Matters (UM) framework that generalizes a Beta-Binomial approach to derive the posterior distribution of any criteria combination, allowing stable performance assessment in a bias-aware setting.We suggest modeling the confusion matrix of each demographic group using a Multinomial distribution updated through a Bayesian procedure. We extend UM to be applicable under the popular K-fold cross-validation procedure. Experiments highlight the benefits of UM over classical evaluation frameworks regarding informativeness and stability.
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