论文标题
评估相似性评分的不确定性：表现与公平性的面部识别
Assessing Uncertainty in Similarity Scoring: Performance & Fairness in Face Recognition
论文作者
论文摘要
ROC曲线不仅是评估性能，而且是评估相似性评分函数的公平属性的主要工具。为了基于经验ROC分析得出可靠的结论，绝对需要评估与ROC曲线的统计版本相关的不确定性水平，这是绝对必要的，尤其是对于具有相当大的社会影响（例如面部识别）的应用。在本文中，我们证明了相似性函数的经验ROC曲线以及副产品指标有用的渐近保证，可用于评估公平性。我们还解释说，由于错误的接受/拒绝率是U统计量的形式，因此在相似性评分的情况下，Naive Bootstrap方法可能会危害评估程序。必须使用专用的近代技术。除了进行理论分析之外，使用真实面部图像数据集进行的各种实验还提供了强有力的经验证据，证明了此处促进的方法的实际相关性，当时将其应用于几种基于ROC的措施，例如流行公平指标。
The ROC curve is the major tool for assessing not only the performance but also the fairness properties of a similarity scoring function. In order to draw reliable conclusions based on empirical ROC analysis, accurately evaluating the uncertainty level related to statistical versions of the ROC curves of interest is absolutely necessary, especially for applications with considerable societal impact such as Face Recognition. In this article, we prove asymptotic guarantees for empirical ROC curves of similarity functions as well as for by-product metrics useful to assess fairness. We also explain that, because the false acceptance/rejection rates are of the form of U-statistics in the case of similarity scoring, the naive bootstrap approach may jeopardize the assessment procedure. A dedicated recentering technique must be used instead. Beyond the theoretical analysis carried out, various experiments using real face image datasets provide strong empirical evidence of the practical relevance of the methods promoted here, when applied to several ROC-based measures such as popular fairness metrics.
