  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.
