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The idea behind Chernoff faces (or using faces for data visualization) seems good: we humans are very good at distinguishing faces, so we can quickly find group

The idea behind Chernoff faces (or using faces for data visualization) seems good: we humans are very good at distinguishing faces, so we can quickly find groups and outliers if the data is encoded with a face. But we have to be careful with this. Changing facial expressions is not the same as increasing the height of a barplot, we're relating features with expressions and the visualization might express things that you don't want. There is a very famous example for this in "Life in Los Angeles" (1977) by Eugene Turner [1]. Maybe you can infer the data well but in the end this just ends up being a map of angry black people. The choice of features and how to visualize them is clearly racist. [1] https://mapdesign.icaci.org/2014/12/mapcarte-353365-life-in-los-angeles-by-eugene-turner-1977/ https://mapdesign.icaci.org/2014/12/mapcarte-353365-life-in-...

ebg13 6y ago
> The idea behind Chernoff faces (or using faces for data visualization) seems good: we humans are very good at distinguishing faces Except that: 1. We humans are actually _ABYSMAL_ at distinguishing faces (https://en.wikipedia.org/wiki/Cross-race_effect https://en.wikipedia.org/wiki/Cross-race_effect) 2. The ability to differentiate between two things and the ability to translate attributes into metrics are fundamentally so different from each other that any possible truth to the idea instantly becomes wildly irrelevant. https://eagereyes.org/criticism/chernoff-faces https://eagereyes.org/criticism/chernoff-faces
- sukilot 6y ago
  One case of being imperfect isn't "abysmal". Cross facial discrimination is far more accurate than cross-species body discrimination, or wood grain discrimination, or many other things. There are brain regions detected to be dedicated to facial recognition.
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sukilot 6y ago
Huh? That map colored black people dark like their skin, and encoded misery as unhappy faces. The result accurately showed happy white people and unhappy black people. How is it racist to acknowledge the racially biased distribution of suffering?
- gnramires 6y ago
  See the legend. Things are ranked from "Good to bad", (Urban stress, unemployment) and proportion white population is right there in parallel, screaming "White"=="Good". There are a number of other lesser reasons, including conflating regions of high urban stress as "Evil" or "Angry" instead of just unhappy[0]. The face visualization just implies too much of a value judgement on the data -- too correlated with the issue (yet misrepresentative) to be a good idea imo. [0]: Even happy/unhappy may not reflect well at all this variable (again because that's a complicated human emotion, not a simple function of "Health crime and transportation factors"). Edit: I think it's also not necessary to call the map creator (or maybe the map itself) racist, this implies some kind of intentional discrimination (and is quite strong imo), but it does have to me grave mentioned problems.
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  - nxpnsv 6y ago
    So you would be ok if row order of columns were randomized?
    - gnramires 6y ago
      I would find it better if the proportion white population were placed horizontally or elsewhere entirely. The other comment noted those metrics are "objective" and only "High"/"Low", but in all cases the least desirable situation (i.e. "bad") is the lowest. I read charts and benchmarks all the time and a usual cognitive shortcut you look for is good/bad (e.g. you see a decreasing graph -- if it's latency that is "low"=="good"; if it were profits it's "low"=="bad"), I'd be surprised if people didn't make similar quick assessments. Note that high white rate is also up, which is usually good too (profits, growth, etc.). I guess no representation is perfect, but I think at least ethnicity could/should be separated here.
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outworlder 6y ago
> Turner does a good job of building a facial profile out of social conditions and ethnicity. It’s a simple map but one that characterises the spatial structure of socio-economic life in Los Angeles. It’s also a provocative and arresting image and one which is difficult to hide from. That's a very successful application if you ask me. It shows unhappy black people. They are displayed as unhappy because they are unhappy. The focus should be on how to make them happy, not 'racism'. It is displaying the effects of racism for all to see.

ebg13 6y ago

> The idea behind Chernoff faces (or using faces for data visualization) seems good: we humans are very good at distinguishing faces Except that: 1. We humans are actually _ABYSMAL_ at distinguishing faces (https://en.wikipedia.org/wiki/Cross-race_effect https://en.wikipedia.org/wiki/Cross-race_effect) 2. The ability to differentiate between two things and the ability to translate attributes into metrics are fundamentally so different from each other that any possible truth to the idea instantly becomes wildly irrelevant. https://eagereyes.org/criticism/chernoff-faces https://eagereyes.org/criticism/chernoff-faces

sukilot 6y ago
One case of being imperfect isn't "abysmal". Cross facial discrimination is far more accurate than cross-species body discrimination, or wood grain discrimination, or many other things. There are brain regions detected to be dedicated to facial recognition.
- deleted 6y ago
  [deleted]

sukilot 6y ago

Huh? That map colored black people dark like their skin, and encoded misery as unhappy faces. The result accurately showed happy white people and unhappy black people. How is it racist to acknowledge the racially biased distribution of suffering?

gnramires 6y ago
See the legend. Things are ranked from "Good to bad", (Urban stress, unemployment) and proportion white population is right there in parallel, screaming "White"=="Good". There are a number of other lesser reasons, including conflating regions of high urban stress as "Evil" or "Angry" instead of just unhappy[0]. The face visualization just implies too much of a value judgement on the data -- too correlated with the issue (yet misrepresentative) to be a good idea imo. [0]: Even happy/unhappy may not reflect well at all this variable (again because that's a complicated human emotion, not a simple function of "Health crime and transportation factors"). Edit: I think it's also not necessary to call the map creator (or maybe the map itself) racist, this implies some kind of intentional discrimination (and is quite strong imo), but it does have to me grave mentioned problems.
- deleted 6y ago
  [deleted]
- nxpnsv 6y ago
  So you would be ok if row order of columns were randomized?
  - gnramires 6y ago
    I would find it better if the proportion white population were placed horizontally or elsewhere entirely. The other comment noted those metrics are "objective" and only "High"/"Low", but in all cases the least desirable situation (i.e. "bad") is the lowest. I read charts and benchmarks all the time and a usual cognitive shortcut you look for is good/bad (e.g. you see a decreasing graph -- if it's latency that is "low"=="good"; if it were profits it's "low"=="bad"), I'd be surprised if people didn't make similar quick assessments. Note that high white rate is also up, which is usually good too (profits, growth, etc.). I guess no representation is perfect, but I think at least ethnicity could/should be separated here.

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outworlder 6y ago

> Turner does a good job of building a facial profile out of social conditions and ethnicity. It’s a simple map but one that characterises the spatial structure of socio-economic life in Los Angeles. It’s also a provocative and arresting image and one which is difficult to hide from. That's a very successful application if you ask me. It shows unhappy black people. They are displayed as unhappy because they are unhappy. The focus should be on how to make them happy, not 'racism'. It is displaying the effects of racism for all to see.