打破全球北方刻板印象：一个以全球南方为中心的基准数据集，用于审计和缓解面部识别系统中的偏差
Breaking the Global North Stereotype: A Global South-centric Benchmark Dataset for Auditing and Mitigating Biases in Facial Recognition Systems
AI总结：
提出一个以全球南方为中心的多样化面部数据集，用于审计商业和开源面部识别系统的性别预测偏差，并利用少样本和对比学习技术设计低资源偏差缓解方案，显著降低了性别间的性能差异。
摘要：
面部识别系统（FRSs）正以前所未有的速度在全球范围内被开发和部署。大多数平台在少数几个国家设计，却部署到全球，且缺乏足够的检查点。这对于全球南方国家尤其成问题，这些国家缺乏强有力的立法来保护面临这些系统性能差异的个人。数据集的不可用、对FRS功能缺乏了解以及低资源偏差缓解措施加剧了这一问题。在这项工作中，我们提出了一个新的面部数据集，由来自全球八个国家的6,579名独特的男女运动员组成。超过50%的数据集由来自全球南方国家的个体组成，并且在人口统计上是多样化的。为了辅助对抗性审计和鲁棒模型训练，每张图像有四个对抗性变体，总计超过40,000张图像。我们还在性别预测任务（以及其中一个开源模型的国家预测任务作为红队测试的例子）上对五个流行的FRSs进行了基准测试，包括商业和开源系统。对工业FRSs的实验显示准确率从98.2%到38.1%不等，在全球南方的男性和女性之间存在巨大差异（最大差异为38.5%）。在所有FRSs中也观察到全球北方和南方女性之间的偏差（最大差异约为50%）。Grad-CAM分析将鼻子、额头和嘴巴识别为其中一个开源FRSs上的感兴趣区域。利用这一见解，我们设计了简单、低资源的偏差缓解解决方案，使用少样本和新型对比学习技术，显著提高了准确率，在一种设置中，男性和女性之间的差异从50%降低到1.5%。在使用开源Deepface模型的红队实验中，对比学习被证明比简单微调更有效。
英文摘要：
Facial Recognition Systems (FRSs) are being developed and deployed globally at unprecedented rates. Most platforms are designed in a limited set of countries but deployed in worldwide, without adequate checkpoints. This is especially problematic for Global South countries which lack strong legislation to safeguard persons facing disparate performance of these systems. A combination of unavailability of datasets, lack of understanding of FRS functionality and low-resource bias mitigation measures accentuate the problem. In this work, we propose a new face dataset composed of 6,579 unique male and female sportspersons from eight countries around the world. More than 50% of the dataset comprises individuals from the Global South countries and is demographically diverse. To aid adversarial audits and robust model training, each image has four adversarial variants, totaling over 40,000 images. We also benchmark five popular FRSs, both commercial and open-source, for the task of gender prediction (and country prediction for one of the open-source models as an example of red-teaming). Experiments on industrial FRSs reveal accuracies ranging from 98.2%--38.1%, with a large disparity between males and females in the Global South (max difference of 38.5%). Biases are also observed in all FRSs between females of the Global North and South (max difference of ~50%). Grad-CAM analysis identifies the nose, forehead and mouth as the regions of interest on one of the open-source FRSs. Utilizing this insight, we design simple, low-resource bias mitigation solutions using few-shot and novel contrastive learning techniques significantly improving the accuracy with disparity between males and females reducing from 50% to 1.5% in one of the settings. In the red-teaming experiment with the open-source Deepface model, contrastive learning proves more effective than simple fine-tuning.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
