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I know these studies are common (probably because they are easy to conduct) but they don't measure the intrinsic bias of the interviewer. What they measure is

by swatow 12y ago

I know these studies are common (probably because they are easy to conduct) but they don't measure the intrinsic bias of the interviewer. What they measure is a combination of the intrinsic bias of the interviewer, and the statistical inference that the interviewer does based on race/gender (statistical discrimination). Because a resume is a noisy signal, it is still possible that race/gender contains extra information even after you have seen the resume. For example, suppose people tend to exaggerate, and this exaggeration introduces some randomness. Given the signal "lead some impressive project", there is some probability that the person didn't really lead the project. Now if the probability of exaggerating is the same, but a Black person or woman was less likely to have lead the project a priori, then even after observing the resume that claims to have lead the project, the a posteriori probability of having lead the project is lower for the Black person or woman.

airza 12y ago
how do you not understand that "statistical discrimination" is a subtype of "intrinsic bias"?
- swatow 12y ago
  Many people, perhaps not you, are interested in distinguishing between discrimination based on Bayesian inference, and discrimination that isn't, and is instead, for example, based on incorrect views or inherent dislike of certain groups. You might say that both are equally bad. I suspect that if you were given the opportunity to argue that a particular form of discrimination is not justified by Bayesian inference, you would do so. It's only because I'm suggesting that it's possible discrimination is justified by Bayesian inference, that you are (rudely) objecting to the distinction.
domdip 12y ago
You say Bayesian inference, I say bigotry.
- drpgq 12y ago
  I think unfortunately Bayesian inference often does mean bigotry. And bigots aren't necessarily always wrong.
  - mercer 12y ago
    I think what would really help these kinds of discussions is to first agree on whether we're discussion a 'should' or an 'is'. In the latter case, there are many conclusions one can draw that are considered racist, misogynist, or discriminatory in any other way. And this knowledge can be valuable for study, or other ways. In the former case, however, all of that doesn't necessarily matter, partly because we assume the reasons for things being as they are, are a result of unfair processes, and partly because we prefer to give the individual the benefit of the doubt over trusting on the statistics that would put this individual in a group that he might not really be part of. I often feel that both types of discussions are significantly harmed by conflating the two.
afarrell 12y ago
I thought (and it occurs to me I have no evidence for this and don't know where the belief came from) that probability of exaggerating was lower for women and black folks.
mturmon 12y ago
Responding to your highly inflammatory hypothetical: You have gone from a nostrum about an entire population ("...a Black person or woman was less likely to have lead the project a priori..."), which could have included thousands-to-millions of people, to a statement about one particular individual. The grossest error of this way of thinking is that it is mixing a vague, dubious, and unquantified signal (your a priori "knowledge") with a very high-quality signal (a specific and verifiable statement made by a single person about a single project). If you're really proposing to do some kind of "Bayesian" weighting of these two pieces of knowledge, you're trusting your machinery for assessment of probabilities way too much. That a priori knowledge is junk compared to the statement on the resume. Or, to look at it the other way round: If you're so well-calibrated that you're taking population-wide information into account, I shudder to think what you must be doing with other side information like the font, page layout, semicolon count, or paper composition. Lump it into the prior! What could possibly go wrong?! I must add that you're deploying a hyper-logical argument in a real-world situation in what is honestly a stupid fashion. Nobody who does real-world inference should operate this way.
- swatow 12y ago
  We are talking about academic studies, not how I would personally act. You are asserting that the signal from race/gender is very noisy and the signal from the resume is very precise. We can debate the precision of the signal from the resume, but race at least is highly predictive of many objective qualities, e.g. it is highly correlated with IQ. So what you call a vague, dubious, and unquantified signal is actually a highly informative signal.
  - mturmon 12y ago
    I notice you went right from race being correlated with IQ to race being predictive of IQ. Unsurprising.
    - swatow 12y ago
      This is a discussion on probability and statistics, so I was using the technical terms. In statistics if A and B are correlated, then A predicts B. But you're just a liberal who assumes everyone who isn't is dumb. Fuck you.

by swatow 12y ago

airza 12y ago

how do you not understand that "statistical discrimination" is a subtype of "intrinsic bias"?

swatow 12y ago
Many people, perhaps not you, are interested in distinguishing between discrimination based on Bayesian inference, and discrimination that isn't, and is instead, for example, based on incorrect views or inherent dislike of certain groups. You might say that both are equally bad. I suspect that if you were given the opportunity to argue that a particular form of discrimination is not justified by Bayesian inference, you would do so. It's only because I'm suggesting that it's possible discrimination is justified by Bayesian inference, that you are (rudely) objecting to the distinction.

domdip 12y ago

You say Bayesian inference, I say bigotry.

drpgq 12y ago
I think unfortunately Bayesian inference often does mean bigotry. And bigots aren't necessarily always wrong.
- mercer 12y ago
  I think what would really help these kinds of discussions is to first agree on whether we're discussion a 'should' or an 'is'. In the latter case, there are many conclusions one can draw that are considered racist, misogynist, or discriminatory in any other way. And this knowledge can be valuable for study, or other ways. In the former case, however, all of that doesn't necessarily matter, partly because we assume the reasons for things being as they are, are a result of unfair processes, and partly because we prefer to give the individual the benefit of the doubt over trusting on the statistics that would put this individual in a group that he might not really be part of. I often feel that both types of discussions are significantly harmed by conflating the two.

afarrell 12y ago

I thought (and it occurs to me I have no evidence for this and don't know where the belief came from) that probability of exaggerating was lower for women and black folks.

mturmon 12y ago

Responding to your highly inflammatory hypothetical: You have gone from a nostrum about an entire population ("...a Black person or woman was less likely to have lead the project a priori..."), which could have included thousands-to-millions of people, to a statement about one particular individual. The grossest error of this way of thinking is that it is mixing a vague, dubious, and unquantified signal (your a priori "knowledge") with a very high-quality signal (a specific and verifiable statement made by a single person about a single project). If you're really proposing to do some kind of "Bayesian" weighting of these two pieces of knowledge, you're trusting your machinery for assessment of probabilities way too much. That a priori knowledge is junk compared to the statement on the resume. Or, to look at it the other way round: If you're so well-calibrated that you're taking population-wide information into account, I shudder to think what you must be doing with other side information like the font, page layout, semicolon count, or paper composition. Lump it into the prior! What could possibly go wrong?! I must add that you're deploying a hyper-logical argument in a real-world situation in what is honestly a stupid fashion. Nobody who does real-world inference should operate this way.

swatow 12y ago
We are talking about academic studies, not how I would personally act. You are asserting that the signal from race/gender is very noisy and the signal from the resume is very precise. We can debate the precision of the signal from the resume, but race at least is highly predictive of many objective qualities, e.g. it is highly correlated with IQ. So what you call a vague, dubious, and unquantified signal is actually a highly informative signal.
- mturmon 12y ago
  I notice you went right from race being correlated with IQ to race being predictive of IQ. Unsurprising.
  - swatow 12y ago
    This is a discussion on probability and statistics, so I was using the technical terms. In statistics if A and B are correlated, then A predicts B. But you're just a liberal who assumes everyone who isn't is dumb. Fuck you.