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This isn't a study that shows that people's faces indicate their political leanings. It's a study which shows that pictures that people select to represent themselves publicly have features that indicate political leaning.
So, unless I read the abstract wrong, this is identifying age, race, and gender, then making categorical qualifiers based on those distinctions. While using ML to do the facial recognition to distinguish a person's age, race and gender is neat - categorizing their political affiliation with a 72% accuracy rate is fairly nominal, given the tools used by modern parties to garner donations and directed online advertisin…
"VGGFace224 was used to convert facial images into face descriptors, or 2,048-value-long vectors subsuming their core features." So they had more than age, race, and gender, but it doesn't really say how things were weighted.
Doesn't have to be that way. It could be age, age+1, age+2 ....
This skims somewhat close to people who tried to infer criminality from facial and cranial characteristics... a century ago
Edit: One last rant about this palm-reading-esque pseudoscience: I hate that this was put out in the universe, and thus potentially giving the wrong person ideas.
I only skimmed the paper, so I'm not claiming to know much about it, but one thing to keep in mind here is that a fair coin has a 50% accuracy using the same terminology as the headline. I'm not saying 72% is not an interesting achievement, its just that "you can do about 50% better than random chance" describes my gut feeling about how much you could actually see in someones face.
It says in the article that humans got just 55% (so 10% better than random chance) on the same test.
I only skimmed the paper, so I'm not claiming to know much about it, but one thing to keep in mind here is that a fair coin has a 50% accuracy using the same terminology as the headline. I'm not saying 72% is not an interesting achievement, its just that "you can do about 50% better than random chance" describes my gut feeling about how much you could actually see in someones face.
It's doing something that untrained humans are not capable of [edited to add: although humans were apparently tested by a different method, so this is not properly comparable], but is still a failing grade by usual methods of assessing human knowledge.
So, unless I read the abstract wrong, this is identifying age, race, and gender, then making categorical qualifiers based on those distinctions. While using ML to do the facial recognition to distinguish a person's age, race and gender is neat - categorizing their political affiliation with a 72% accuracy rate is fairly nominal, given the tools used by modern parties to garner donations and directed online advertisin…
They tested this question specifically: > Both in real life and in our sample, the classification of political orientation is to some extent enabled by demographic traits clearly displayed on participants’ faces. For example ... white people, older people, and males are more likely to be conservatives. What would an algorithm’s accuracy be when distinguishing between faces of people of the same age, gender, and ethni…
- The rest of the word.
Earlier quoted context omitted.
More echo chamber...
Can you explain to me how that’s a bad thing? Differences among people do not make them more United and their bonds stronger. Literally the opposite is true.