Mr. Owl, how many licks does it take to get to the Tootsie Roll center of a Tootsie Pop?
A good question. Let’s find out. One, two, three…(bites) three.
From YouTube.
If you think Mr. Owl’s conclusion was flawed, let’s look at Google.
One, two, three…three
I was researching the Monk Skin Tone Scale for a future Bredemarket blog post, but before I share that post I have to respond to an inaccurate statement from Google.
Google began its page “Developing the Monk Skin Tone Scale” with the following statement:
In 2018, the pioneering Gender Shades study demonstrated that commercial, facial-analysis APIs perform substantially worse on images of people of color and women.
Um…no it didn’t.
I will give Google props for using the phrase “facial-analysis,” which clarifies that Gender Shades was an exercise in categorization, not individualization.
But to say that Gender Shades “demonstrated that commercial, facial-analysis APIs perform substantially worse” in certain situations is an ever-so-slight exaggeration.
Kind of like saying that a bad experience at a Mexican restaurant in Lusk, Wyoming demonstrates that all Mexican restaurants are bad.
How? I’ve said this before:
The Gender Shades study evaluated only three algorithms: one from IBM, one from Microsoft, and one from Face++. It did not evaluate the hundreds of other facial recognition algorithms that existed in 2018 when the study was released.
So to conclude that all facial classification algorithms perform substantially worse cannot be supported…because in 2018 the other algorithms weren’t tested.
One, two, three…one hundred and eighty nine
In 2019, NIST tested 189 software algorithms from 99 developers for demographic bias, and has continued to test for demographic bias since.
In these tests, vendors volunteer to have NIST test their algorithms for demographic bias.
Guess which three vendors have NOT submitted their algorithms to NIST for testing?
You guessed it: IBM, Microsoft, and Face++.
Anyway, more on the Monk Skin Tone Scale here, but I had to share this.

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