Did it? If you control for gender but not sex, you can use the difference to predict ideology. And for ethnicity, there are subethnicities that matter too - white Italian and white German have different proclivities.
Simplistically, let’s take the above statistic “A randomly chosen black individual in the united states has a 72% chance of leaning democrat” at face value. So, a coin flip would be lower than 50-50 because someone of that race in that country does not have a 50 50 chance. So you would adjust the chance to 72-28 and compare that to the Facial recognition results. If you find that the results are the same, then you know that the Facial Recognition not picking up on anything beyond race. If the results are different, you know the FR is picking up on something in addition to race.
Really it is more complex than that, but fundamentally you try to say “how accurate can we be using just age, gender, and ethnicity” and use that as your controlled benchmark.
I understand what they're implying by "adjusted accuracy". My point is that I'm not sure that metric really makes sense, because "accuracy" isn't a particularly useful metric to begin with. It depends entirely on the sample distribution. "Always guess not fraud" will be 99.9% "accurate" for most use cases.
I'm asking what the literal metric is.
edit: and I don't think your explanation really works for accuracy, because accuracy isn't a relative measure, like, say, R2.
> The accuracy is expressed as AUC, or a fraction of correct guesses when distinguishing between all possible pairs of faces—one conservative and one liberal.
I've never seen something like this. Maybe this is a normal procedure?
But I would be worried that the number of old black conservative women would be really small. Seems a bit sketchy