[Defenestresque]

This is the exact question I came to the comments to find.

The abstract states:

>Accuracy remained high (69%) even when controlling for age, gender, and ethnicity.

To give some context, chance is 50%, human guess is 55% and a 100-question questionnaire is 66%.

Personally, I am surprised that the accuracy remained that high when controlling for the three variables I would have considered most telling in the determination (age, gender and race).

I'd be very curious to know what exactly the algorithm is determining from the face photos outside of those obvious variables. I know with a ML algorithm it's practically impossible to determine why the classification was made, but does anyone human here have any thoughts?

[EGreg]

In fact, I'd put it another way. I'm surprised the accuracy was not higher when you ADDED IN the three variables to the 69%.

Could it be a version of this: https://hackernoon.com/dogs-wolves-data-science-and-why-mach...