商业人脸检测模型与学术模型一样有偏见吗？
Are Commercial Face Detection Models as Biased as Academic Models?
摘要：
随着面部识别系统的广泛部署，学者和活动家们已经研究了它们的偏见和危害。审计通常用于实现这一点，并将算法面部识别系统的性能与具有关于图像主题的各种元数据标签的数据集进行比较。这些研究和审计经常检查算法，这些算法分为两类：学术模型或商业模型。我们提出了一个详细的学术和商业人脸检测系统之间的比较，特别是检查噪声的鲁棒性。我们发现，最先进的学术人脸检测模型在其噪声鲁棒性方面表现出人口统计学差异，特别是在老年人和以男性化方式呈现性别的人身上表现出统计学显著下降。当我们将这些差异的大小与商业模型进行比较时，我们得出结论，商业模型--与其相对较大的开发预算和行业层面的公平承诺相比--总是比学术模型有偏见或更有偏见。
英文摘要：
As facial recognition systems are deployed more widely, scholars and activists have studied their biases and harms. Audits are commonly used to accomplish this and compare the algorithmic facial recognition systems' performance against datasets with various metadata labels about the subjects of the images. Seminal works have found discrepancies in performance by gender expression, age, perceived race, skin type, etc. These studies and audits often examine algorithms which fall into two categories: academic models or commercial models. We present a detailed comparison between academic and commercial face detection systems, specifically examining robustness to noise. We find that state-of-the-art academic face detection models exhibit demographic disparities in their noise robustness, specifically by having statistically significant decreased performance on older individuals and those who present their gender in a masculine manner. When we compare the size of these disparities to that of commercial models, we conclude that commercial models - in contrast to their relatively larger development budget and industry-level fairness commitments - are always as biased or more biased than an academic model.
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