Automated gender classification has important applications in many domains, such as demographic research, law enforcement, online advertising, as well as human-computer interaction. Recent research has questioned the fairness of this technology across gender and race. Specifically, the majority of the studies raised the concern of higher error rates of the face-based gender classification system for darker-skinned people like African-American and for women. However, to date, the majority of existing studies were limited to African-American and Caucasian only. The aim of this paper is to investigate the differential performance of the gender classification algorithms across gender-race groups. To this aim, we investigate the impact of (a) architectural differences in the deep learning algorithms and (b) training set imbalance, as a potential source of bias causing differential performance across gender and race. Experimental investigations are conducted on two latest large-scale publicly available facial attribute datasets, namely, UTKFace and FairFace. The experimental results suggested that the algorithms with architectural differences varied in performance with consistency towards specific gender-race groups. For instance, for all the algorithms used, Black females (Black race in general) always obtained the least accuracy rates. Middle Eastern males and Latino females obtained higher accuracy rates most of the time. Training set imbalance further widens the gap in the unequal accuracy rates across all gender-race groups. Further investigations using facial landmarks suggested that facial morphological differences due to the bone structure influenced by genetic and environmental factors could be the cause of the least performance of Black females and Black race, in general.

中文翻译：

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理解跨性别种族群体的性别分类算法的公平性

自动性别分类在许多领域都有重要应用，例如人口研究、执法、在线广告以及人机交互。最近的研究质疑这项技术对性别和种族的公平性。具体而言，大多数研究都提出了基于面部的性别分类系统对于非洲裔美国人等肤色较深的人和女性的错误率较高的担忧。然而，迄今为止，大多数现有研究仅限于非裔美国人和白种人。本文的目的是研究性别分类算法在不同性别种族群体中的不同性能。为此，我们研究了（a）深度学习算法中的架构差异和（b）训练集不平衡的影响，作为导致跨性别和种族表现差异的潜在偏见来源。对两个最新的大规模公开面部属性数据集进行了实验研究，即 UTKFace 和 FairFace。实验结果表明，具有架构差异的算法在性能上有所不同，并且对特定性别种族群体具有一致性。例如，对于所有使用的算法，黑人女性（一般为黑人）总是获得最低的准确率。大多数情况下，中东男性和拉丁裔女性获得了更高的准确率。训练集不平衡进一步扩大了所有性别种族群体之间不等的准确率差距。