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> The issue is that as a society we've (mostly) decided that unfairly classifying someone based on correlated but non-causal characteristics is wrong, EVEN in t

by fao_ 7y ago

> The issue is that as a society we've (mostly) decided that unfairly classifying someone based on correlated but non-causal characteristics is wrong, EVEN in the extreme that you're right more often than you're wrong if you make that assumption. Sorry, but it's not a decision. Science has found repeatedly that using outward characteristics does not work as a good classification measure. Society simply enforces not making bad judgements. See: Phrenology, Racism -- "appearance implies certain things about someone's character and intelligence" (https://en.wikipedia.org/wiki/Phrenology https://en.wikipedia.org/wiki/Phrenology) Sex -- "A person's gender presentation determines their gender or sex" (https://en.wikipedia.org/wiki/Transgender_history https://en.wikipedia.org/wiki/Transgender_history), "A person's genitalia determines their chromosones", "A person's chromosones determine their sex and their ability to give birth" (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2190741/ https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2190741/). Wealth -- "That person is wearing bad clothing/living an unassuming life therefore they must be poor" (https://www.npr.org/2018/12/29/680883772/social-worker-led-frugal-life-to-donate-nearly-11-million-to-childrens-charities https://www.npr.org/2018/12/29/680883772/social-worker-led-f...) Intelligence -- "This person scores badly on intelligence tests therefore they must not be worthwhile" (https://en.wikipedia.org/wiki/Richard_Feynman https://en.wikipedia.org/wiki/Richard_Feynman, https://www.psychologytoday.com/gb/blog/sudden-genius/201101/is-high-intelligence-necessary-be-genius https://www.psychologytoday.com/gb/blog/sudden-genius/201101...), "This person appears to be unskilled or incapable of many tasks therefore they must be totally unskilled" (http://en.wikipedia.org/wiki/Savant_syndrome http://en.wikipedia.org/wiki/Savant_syndrome)

deogeo 7y ago
> Science has found repeatedly that using outward characteristics does not work as a good classification measure. Science has found that, on average, it works [1]. Of course there will be many cases where it fails - that's how statistical inference works. Whether that makes it a 'good' measure, by whatever standard, is a different question. But there is no doubt information can be inferred from appearance. [1] http://www.spsp.org/news-center/blog/stereotype-accuracy-response http://www.spsp.org/news-center/blog/stereotype-accuracy-res...
Enginerrrd 7y ago
>Sorry, but it's not a decision. Science has found repeatedly that using outward characteristics does not work as a good classification measure. Lol, what? Generically, that statement is almost certainly false more often than true in general in science. But I believe you are restricting yourself to social psychology? You then presented a long list of examples taken from the tails of certain distributions to refute an argument that said distribution exists and has an average? I didn't even name any particular distributions. You're thinking appears flawed and emotionally driven, and most unfortunately, that's the type of thinking that will lead you to building biased systems. Here's the point you missed the first time around: There are going to be outwardly visible characteristics that ARE correlated with some factor of interest, to the extent that training a machine learning algorithm based on a cost function that uses predictive accuracy alone WILL result in a system that assigns what society would consider an inappropriate importance placed on non-causal but correlated parameters. Here's a real world example that might help you understand why this is important: (Data taken from: https://en.wikipedia.org/wiki/Incarceration_in_the_United_States#Ethnicity https://en.wikipedia.org/wiki/Incarceration_in_the_United_St...) Because blacks are over-represented in the US criminal justice system (40% of the prison population vs 13% of the population) and because part of what defines "black" is the outward appearance of certain facial features, a facial-recognition algorithm which is trained to recognize criminals, with a cost function based on prediction accuracy alone, and facial features as input parameters is going to have false positives that over-represent blacks. Does that sound like something you want? Because denying the underlying distributions is going to lead to exactly that. It's very important to consider this when you develop a training set, for fucks sake. It might work something like this: Take 100 innocent people's faces at random. (On average it will have only 13 blacks) Then take 100 random criminal faces from inmates. (On average it will have 40 blacks.) Then mix up the groups into your training set and assign a prediction score 1 or 0 depending on whether or not your classifier has correctly predicted whether or not a face was in the criminal group. Then, based on no other feature than race, your neural net can get better performance based solely on guessing more often that black people are criminals. That's not a good thing. Do you get it now? The likelihood of being falsely identified as a criminal is greater based on the non-causal but correlated variable of being black. And this has happened several times already! You can't keep pushing this narrative that neglects the underlying statistics because of your beliefs, or people will keep making racist systems.
perl4ever 7y ago
You're saying correlation is not causation, a very well known truism. Surely you can't be seriously saying that society or anything else effectively prevents people from equating the two?
Erlich_Bachman 7y ago
You have such poor use of sources to underline your claims it is almost incredible. What underlines it is seemingly a lack of understanding of statistics, becausee the most "examples", if they can even be called that are from the realm of non-common outlier cases. That's not what statistics operates on mostly - it operates on common cases that cover the largest part of a distribution. Not outlier cases on the ends of a distribution. In every case you gave an example of some uncommon exception, as if that would somehow be an argument for the rule existing?

by fao_ 7y ago

deogeo 7y ago

> Science has found repeatedly that using outward characteristics does not work as a good classification measure. Science has found that, on average, it works [1]. Of course there will be many cases where it fails - that's how statistical inference works. Whether that makes it a 'good' measure, by whatever standard, is a different question. But there is no doubt information can be inferred from appearance. [1] http://www.spsp.org/news-center/blog/stereotype-accuracy-response http://www.spsp.org/news-center/blog/stereotype-accuracy-res...

Enginerrrd 7y ago

>Sorry, but it's not a decision. Science has found repeatedly that using outward characteristics does not work as a good classification measure. Lol, what? Generically, that statement is almost certainly false more often than true in general in science. But I believe you are restricting yourself to social psychology? You then presented a long list of examples taken from the tails of certain distributions to refute an argument that said distribution exists and has an average? I didn't even name any particular distributions. You're thinking appears flawed and emotionally driven, and most unfortunately, that's the type of thinking that will lead you to building biased systems. Here's the point you missed the first time around: There are going to be outwardly visible characteristics that ARE correlated with some factor of interest, to the extent that training a machine learning algorithm based on a cost function that uses predictive accuracy alone WILL result in a system that assigns what society would consider an inappropriate importance placed on non-causal but correlated parameters. Here's a real world example that might help you understand why this is important: (Data taken from: https://en.wikipedia.org/wiki/Incarceration_in_the_United_States#Ethnicity https://en.wikipedia.org/wiki/Incarceration_in_the_United_St...) Because blacks are over-represented in the US criminal justice system (40% of the prison population vs 13% of the population) and because part of what defines "black" is the outward appearance of certain facial features, a facial-recognition algorithm which is trained to recognize criminals, with a cost function based on prediction accuracy alone, and facial features as input parameters is going to have false positives that over-represent blacks. Does that sound like something you want? Because denying the underlying distributions is going to lead to exactly that. It's very important to consider this when you develop a training set, for fucks sake. It might work something like this: Take 100 innocent people's faces at random. (On average it will have only 13 blacks) Then take 100 random criminal faces from inmates. (On average it will have 40 blacks.) Then mix up the groups into your training set and assign a prediction score 1 or 0 depending on whether or not your classifier has correctly predicted whether or not a face was in the criminal group. Then, based on no other feature than race, your neural net can get better performance based solely on guessing more often that black people are criminals. That's not a good thing. Do you get it now? The likelihood of being falsely identified as a criminal is greater based on the non-causal but correlated variable of being black. And this has happened several times already! You can't keep pushing this narrative that neglects the underlying statistics because of your beliefs, or people will keep making racist systems.

perl4ever 7y ago

You're saying correlation is not causation, a very well known truism. Surely you can't be seriously saying that society or anything else effectively prevents people from equating the two?

Erlich_Bachman 7y ago

You have such poor use of sources to underline your claims it is almost incredible. What underlines it is seemingly a lack of understanding of statistics, becausee the most "examples", if they can even be called that are from the realm of non-common outlier cases. That's not what statistics operates on mostly - it operates on common cases that cover the largest part of a distribution. Not outlier cases on the ends of a distribution. In every case you gave an example of some uncommon exception, as if that would somehow be an argument for the rule existing?