Are there no fair algorithms? I urge the authors to at least give Bogosort another chance!
The authors would consider no algorithm fair that doesn't discriminate based on ethnicity, sex, sexuality, religion, etc.
What If Algorithms Could Be Fair?
21–30 of 33 posts
Re: What If Algorithms Could Be Fair?
#22Earlier quoted context omitted.
If more than an average number of Italians are fast drivers, it doesn't mean being Italian causes being a fast driver. Is the idea that correlation is not causation in this context really breaking everybody's brain? Now you may argue that correlation reflects causation in a particular case, sure, but in general , it is not the same, so it seems perfectly logical to me to point out that you can start building your mod…
Is it so hard for you to believe there might plausibly be a causation? Consider the case of African Americans who are discriminated against by traffic cops. Is it plausible that African Americans, in an attempt [perhaps in vain] to minimize interaction with traffic cops, are more cognizant of traffic laws and drive more conservatively than the average American? I don't know if the data supports that hypothetical, but…
The elephant in the room is that the real way to tell whether that is the case would be to use race as a factor the same as age or sex. If African Americans are more careful drivers then that would detect it and take it into account.
But then you have to take the bad with the good. If it turns out that strict adherence to traffic laws that nobody else abides is actually more dangerous than following the normal flow of traffic, it would also detect that and take it into account.
Re: What If Algorithms Could Be Fair?
#23Earlier quoted context omitted.
If more than an average number of Italians are fast drivers, it doesn't mean being Italian causes being a fast driver. Is the idea that correlation is not causation in this context really breaking everybody's brain? Now you may argue that correlation reflects causation in a particular case, sure, but in general , it is not the same, so it seems perfectly logical to me to point out that you can start building your mod…
Is it so hard for you to believe there might plausibly be a causation? Consider the case of African Americans who are discriminated against by traffic cops. Is it plausible that African Americans, in an attempt [perhaps in vain] to minimize interaction with traffic cops, are more cognizant of traffic laws and drive more conservatively than the average American? I don't know if the data supports that hypothetical, but…
Whether you or I think there is a causation in specific cases is irrelevant, as is whether we apply charged terms like "racism" to certain causal linkages.
The point is that one is not compelled to believe in the causal link just because there is a statistical link.
So if certain causal links are politically contentious, rejecting them due to "political correctness" is completely separate from rejecting the facts, the statistics that are collected. It is political, but not in opposition to reality.
The article, as I understood it, is puncturing the assertion of objectivity by those who implicitly assert we have to regard all correlations as equally causal or else be against reason and logic.
Re: What If Algorithms Could Be Fair?
#24Earlier quoted context omitted.
I think the whole point is that what causal relationships you assume matter, and they do not have to be derived from correlations. And they should not, in order to be "fair". You have a choice of whether or not you believe being male causes car insurance claims. That is independent of the statistical correlations. Ten times a day people say correlation is not causation, but a hundred times a day, I see people implici…
It's not that people think correlation implies causation, as much as in many practical models, it's correlations you care about, not causation. If I'm running an insurance agency and not a public policy advocacy, and my data keeps showing that men have higher accident rate than women, I can just ignore causation and build my actuarial tables based on that. I don't need a casual model here, at least not until the poin…
This makes no sense to me. Everything depends on your causal model. You can't just not have one; if you don't have one, you are treating correlations as causative indiscriminately.
Suppose (just as a toy example) that being young causes accidents, and the population of men is younger, but being male does not cause accidents. You are going to charge mature men too much and lose that business to a competitor with a correct causal model.
This is quite separate from the correlational data.
The insight I get from the article is that the "correctness" of your causal model can incorporate social justice or political correctness, without being objectively mistaken, because causation is not defined by measured correlations.
Re: What If Algorithms Could Be Fair?
#25Earlier quoted context omitted.
Is it so hard for you to believe there might plausibly be a causation? Consider the case of African Americans who are discriminated against by traffic cops. Is it plausible that African Americans, in an attempt [perhaps in vain] to minimize interaction with traffic cops, are more cognizant of traffic laws and drive more conservatively than the average American? I don't know if the data supports that hypothetical, but…
The elephant in the room is that the real way to tell whether that is the case would be to use race as a factor the same as age or sex. If African Americans are more careful drivers then that would detect it and take it into account. But then you have to take the bad with the good. If it turns out that strict adherence to traffic laws that nobody else abides is actually more dangerous than following the normal flow o…
"then that would detect it and take it into account."
You have a method for automatically deriving causal relationships from correlational data?
Re: What If Algorithms Could Be Fair?
#26Earlier quoted context omitted.
"forbidding this type of inference" Isn't this just a misleading way to say "holding a certain causal belief"? Why exactly would that be a bad thing? If you reject one set of causal beliefs, you necessarily hold a different set.
Some beliefs are correlated with reality, others don't. If GP's assertion about 34% more drinking on average is true, then rejecting it isn't "holding a different set of beliefs", it's just being wrong. If there's an issue worth pursuing here, it's educating people to stop using average population statistics to rate individuals from populations. Usually the variance within a population makes population-level statisti…
Rejecting the causal relationship is not the same as rejecting the correlation, right? Why can't (or shouldn't) one separate the two?
Re: What If Algorithms Could Be Fair?
#27Earlier quoted context omitted.
Is it so hard for you to believe there might plausibly be a causation? Consider the case of African Americans who are discriminated against by traffic cops. Is it plausible that African Americans, in an attempt [perhaps in vain] to minimize interaction with traffic cops, are more cognizant of traffic laws and drive more conservatively than the average American? I don't know if the data supports that hypothetical, but…
The elephant in the room is that the real way to tell whether that is the case would be to use race as a factor the same as age or sex. If African Americans are more careful drivers then that would detect it and take it into account. But then you have to take the bad with the good. If it turns out that strict adherence to traffic laws that nobody else abides is actually more dangerous than following the normal flow o…
Well it may be the case that they accidentally have a proxy for race already in their data (the "this ethnicity prefers red cars" hypothetical in the article above.) So race may already in practice be factored in despite nobody intending for that to be the case (assuming nobody anticipated that a particular metric is a racial proxy.) That does not necessarily mean it's being unfair to that race though. It could, hypothetically, mean that it's actually being fair to that race, advantaging them in a system/society that would otherwise disadvantage them.
Re: What If Algorithms Could Be Fair?
#28Earlier quoted context omitted.
Is it so hard for you to believe there might plausibly be a causation? Consider the case of African Americans who are discriminated against by traffic cops. Is it plausible that African Americans, in an attempt [perhaps in vain] to minimize interaction with traffic cops, are more cognizant of traffic laws and drive more conservatively than the average American? I don't know if the data supports that hypothetical, but…
Whether you or I think there is a causation in specific cases is irrelevant, as is whether we apply charged terms like "racism" to certain causal linkages. The point is that one is not compelled to believe in the causal link just because there is a statistical link. So if certain causal links are politically contentious, rejecting them due to "political correctness" is completely separate from rejecting the facts, th…
I certainly do not believe anybody should be compelled to assume a correlation is a causation. However I also do not think one should preemptively rule out the possibility of causation. Without examining the nitty gritty details of any particular situation, we can't know which is the case. We certainly cannot assume one and rule out the other, which I fear is what you assumed I was doing.
Re: What If Algorithms Could Be Fair?
#29Earlier quoted context omitted.
Some beliefs are correlated with reality, others don't. If GP's assertion about 34% more drinking on average is true, then rejecting it isn't "holding a different set of beliefs", it's just being wrong. If there's an issue worth pursuing here, it's educating people to stop using average population statistics to rate individuals from populations. Usually the variance within a population makes population-level statisti…
Rejecting the causal relationship is not the same as rejecting the correlation, right? Why can't (or shouldn't) one separate the two?
You're right in principle, but the point here is about the reasons for rejecting a casual model. The issue people seeking fairness in statistics run into is rejecting models based on what ought to be, instead of what is. A casual model can be totally unfair, and yet also correct (insofar an approximation is considered correct).
Taking the example from our parallel discussion, if the data says being male is correlated with risky driving, and it seems to fit the casual model of "male -> risky", it would be wrong to reject it just on the grounds of "we're using this model to set insurance rates, so by penalizing males, the model is sexist". It may be that you can come up with a better casual model explaining the correlation - say, cultural history and path dependence - but until you can, rejecting a fitting model based on "it's unfair, reality ought not to be so" is just wrong.
Re: What If Algorithms Could Be Fair?
#30Earlier quoted context omitted.
The elephant in the room is that the real way to tell whether that is the case would be to use race as a factor the same as age or sex. If African Americans are more careful drivers then that would detect it and take it into account. But then you have to take the bad with the good. If it turns out that strict adherence to traffic laws that nobody else abides is actually more dangerous than following the normal flow o…
"then that would detect it and take it into account." You have a method for automatically deriving causal relationships from correlational data?
A lack of a causal relationship wouldn't matter in that case. If something correlates with the outcome then it allows you to better predict the outcome even if it isn't the cause, because it at least correlates with the cause or it wouldn't correlate with the outcome.
Though obviously if it isn't the cause then you're better off taking into account the true cause rather than only the thing that correlates with it -- which would cause the correlation with the outcome to disappear for the non-causal factor when you take into account both.