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>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? Ge

>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.

stubish 7y ago
It seems that some tasks are just inherently racist (and sexist, etc.), and we should be able to identify them before someone inflicts it on society. Trying to identify potential for criminality based on looks may well work, identifying and using the underlying racial biases. And if those systems are used, where people identified by these systems are more likely to be identified as criminals and investigated, we end up with a feedback loop and, over time, the racial biases in a society that uses the systems will get worse. More black criminals will be caught, as they are more likely to be suspected as criminal, making the racial biases worse for blacks. While the opposite happening for white faces. Similar social evolution would happen if you pre-screened job candidates, over time magnifying existing gender and racial biases. I've seen in some cultures it is required to add photographs to job applications, but I think it a good thing that this practice is discouraged in western countries.
- bsanr2 7y ago
  This is truly where the danger lies. You can say that you're trying to make rulesets that are accurate to the world we live in, without acknowledging that you fall heir to, and may in fact be producing this generation's version of, historical policy decisions that contributed to or outright created racial division and disparities in the first place. Then and now exist an appeal to empiricism that takes the current state as the natural order, and not one manufactured by past decisions which aimed specifically at a particular, and not altogether organic, conclusion.
- esyir 7y ago
  So, I disagree with the "inherently racist" portion of your argument. You can evaluate the classifiers in a race independent manner, i.e. take a look at the metrics stratified across race. I'm going to proceed here assuming that the classification task is possible. Say in your example of criminality, if black people were more predicted by the classifier, it isn't racist if it accurately reflects the base distribution. There's a line to be crossed here, and to me it's if that application starts to significantly (where the boundary lies here is up for debate), and directly affect the non-criminal portion. I'd say that if the misclassification error is similar across racial lines, then there is no issue. Additionally, I don't quite agree with the "making racial biases worse" argument either. The way I see it, we already use racial heuristics in law enforcement. With automated, replicable tasks, we can at least quantify the degree of bias and correct for such.

stubish 7y ago

It seems that some tasks are just inherently racist (and sexist, etc.), and we should be able to identify them before someone inflicts it on society. Trying to identify potential for criminality based on looks may well work, identifying and using the underlying racial biases. And if those systems are used, where people identified by these systems are more likely to be identified as criminals and investigated, we end up with a feedback loop and, over time, the racial biases in a society that uses the systems will get worse. More black criminals will be caught, as they are more likely to be suspected as criminal, making the racial biases worse for blacks. While the opposite happening for white faces. Similar social evolution would happen if you pre-screened job candidates, over time magnifying existing gender and racial biases. I've seen in some cultures it is required to add photographs to job applications, but I think it a good thing that this practice is discouraged in western countries.

bsanr2 7y ago
This is truly where the danger lies. You can say that you're trying to make rulesets that are accurate to the world we live in, without acknowledging that you fall heir to, and may in fact be producing this generation's version of, historical policy decisions that contributed to or outright created racial division and disparities in the first place. Then and now exist an appeal to empiricism that takes the current state as the natural order, and not one manufactured by past decisions which aimed specifically at a particular, and not altogether organic, conclusion.
esyir 7y ago
So, I disagree with the "inherently racist" portion of your argument. You can evaluate the classifiers in a race independent manner, i.e. take a look at the metrics stratified across race. I'm going to proceed here assuming that the classification task is possible. Say in your example of criminality, if black people were more predicted by the classifier, it isn't racist if it accurately reflects the base distribution. There's a line to be crossed here, and to me it's if that application starts to significantly (where the boundary lies here is up for debate), and directly affect the non-criminal portion. I'd say that if the misclassification error is similar across racial lines, then there is no issue. Additionally, I don't quite agree with the "making racial biases worse" argument either. The way I see it, we already use racial heuristics in law enforcement. With automated, replicable tasks, we can at least quantify the degree of bias and correct for such.