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.

本文研究了基于算法的面部识别系统的偏差和损害，通过比较学术模型和商业模型的噪声鲁棒性发现，学术模型在年龄和男性化性别方面的表现存在明显的种族和性别偏见，与商业模型相比，商业模型在公平性方面的表现并不理想。