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They tested this question specifically: > Both in real life and in our sample, the classification of political orientation is to some extent enabled by demogra

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 ethnicity? To answer this question, classification accuracies were recomputed using only face pairs of the same age, gender, and ethnicity ... The accuracy dropped by only 3.5% on average

11thEarlOfMar 6y ago
From there, it would seem that cues might come from how 'kempt' they appear, whether the head shot was from a party or for a resume, perhaps color and style of clothing, ... I.e., maybe not strictly the face.
- evan_ 6y ago
  If they’re wearing a face mask, if they’re carrying a tiki torch or storming the capitol building...
- tyingq 6y ago
  "To minimize the role of the background and non-facial features, images were tightly cropped around the face" Though cropping can only do so much.
- deleted 6y ago
  [deleted]
kevinventullo 6y ago
If the total population sampling is the same, I would expect the accuracies to remain the same. E.g. if I can get 72% accuracy in the total population just by looking at age/race/gender, doesn’t that exactly mean the accuracies in each individual category are on average 72%?
- mattnewton 6y ago
  Not necessarily because each age/race/gender tuple can be present in the test dataset different amounts, and either be a stronger or weaker indicator to the model.
  - kevinventullo 6y ago
    Still, it’s some kind of weighted average, right? Like that 3.5% drop seems to say more about the test data used than the model performance per se.
rsynnott 6y ago
I suspect there are a lot of less obvious things they'd need to control for. Off the top of my head, weight would be an obvious one; in developed countries urban areas (particularly large urban areas) generally have a lower average BMI than rural and suburban areas, and there's also typically a major political difference between rural and urban areas.
- Leherenn 6y ago
  But wouldn't this be a reasonable feature used by the classifier to reach its conclusion? They can't control for everything, it would become meaningless. I think the questions about age/sex/ethnicity are sensible in that it's a valid question to ask whether it's just doing the naive/obvious thing or something more. But if you keep on removing the less obvious things then of course you'll reach a point where it's no better than a coin flip because it's basically comparing blank pictures.

11thEarlOfMar 6y ago

From there, it would seem that cues might come from how 'kempt' they appear, whether the head shot was from a party or for a resume, perhaps color and style of clothing, ... I.e., maybe not strictly the face.

evan_ 6y ago
If they’re wearing a face mask, if they’re carrying a tiki torch or storming the capitol building...
tyingq 6y ago
"To minimize the role of the background and non-facial features, images were tightly cropped around the face" Though cropping can only do so much.
deleted 6y ago
[deleted]

kevinventullo 6y ago

If the total population sampling is the same, I would expect the accuracies to remain the same. E.g. if I can get 72% accuracy in the total population just by looking at age/race/gender, doesn’t that exactly mean the accuracies in each individual category are on average 72%?

mattnewton 6y ago
Not necessarily because each age/race/gender tuple can be present in the test dataset different amounts, and either be a stronger or weaker indicator to the model.
- kevinventullo 6y ago
  Still, it’s some kind of weighted average, right? Like that 3.5% drop seems to say more about the test data used than the model performance per se.

rsynnott 6y ago

I suspect there are a lot of less obvious things they'd need to control for. Off the top of my head, weight would be an obvious one; in developed countries urban areas (particularly large urban areas) generally have a lower average BMI than rural and suburban areas, and there's also typically a major political difference between rural and urban areas.

Leherenn 6y ago
But wouldn't this be a reasonable feature used by the classifier to reach its conclusion? They can't control for everything, it would become meaningless. I think the questions about age/sex/ethnicity are sensible in that it's a valid question to ask whether it's just doing the naive/obvious thing or something more. But if you keep on removing the less obvious things then of course you'll reach a point where it's no better than a coin flip because it's basically comparing blank pictures.