4 ms·

That's an argument for digging into the inner workings of the hiring process more than an argument for looking at aggregate effect. If you're just looking at ag

That's an argument for digging into the inner workings of the hiring process more than an argument for looking at aggregate effect. If you're just looking at aggregate group statistics about candidate pools and hiring decisions, how would you ever know the difference between your hiring screen preferring a group and the candidate pool genuinely having more strong candidates in that group? It seems more than a little quixotic if fixing every known flaw in the hiring process isn't enough - that the process must get a certain hiring outcome in order to be "fair".

mikeash 9y ago
And if they do dig into the inner workings of the hiring process and the results are still not representative of the general population? Do you just assume you've somehow managed to eliminate all bias and the results must reflect the population? Or do you assume that you haven't managed to 100% debug this highly difficult process? It is very difficult to tell the difference between biased hiring and actual skew in the population. But when it's a trait that has no obvious connection to the job, it seems best to assume the process is biased, unless really good evidence exists to demonstrate a connection. For example, let's say your interview process produces hires whose heights are substantially above average. If you're hiring for a basketball team then this would make perfect sense. If you're hiring Java programmers, it's highly likely that your interview process is screwed up. It's possible that the population of good Java programmers is unusually tall because of some biological factor, but this idea needs some major justification before you use it to make decisions. Obviously, the first thing to do is to examine your interview process and eliminate any source of height bias you can find. But what if you do that and you're still hiring an abnormally large number of tall people? You could take this as sufficient evidence that tall people are better at this, but that's putting an awful lot of confidence in your interview program. It's more likely that you just haven't found all the sources of tall bias. If that's the case, then pushing for more short hires will improve the overall quality of your hires because you won't be artificially excluding good short ones.
- ThrustVectoring 9y ago
  >It's possible that the population of good Java programmers is unusually tall because of some biological factor, but this idea needs some major justification before you use it to make decisions. But "we're somehow secretly and unknowingly discriminating in our hiring process" doesn't need such justification before using it to make decisions?
  - mikeash 9y ago
    Correct, because bias is prevalent and almost impossible to avoid. For example: does any part of your interview involve a subjective evaluation? If so, do any of the evaluators know the candidates' names? If so, congratulations, your process is probably biased! http://www.politifact.com/punditfact/statements/2015/mar/15/jalen-ross/black-name-resume-50-percent-less-likely-get-respo/ http://www.politifact.com/punditfact/statements/2015/mar/15/... That's literally all it takes: some human judgment and some way to guess at the applicant's race. The discrimination can happen subconsciously. You and I would probably fall victim to it despite our best intentions. Since the manifesto we're discussing is mostly about gender, it's worth mentioning that the same thing happens there: http://m.pnas.org/content/109/41/16474.abstract http://m.pnas.org/content/109/41/16474.abstract Nobody knows how to screen job applicants for pure merit without bias. If it's a choice between "our process is imperfect just like everybody else's" and "we stumbled upon the perfect unbiased screening process and all bias in the output is due to inherent variation in the population," bet on the first one every time. It's about a million times more likely.

mikeash 9y ago

And if they do dig into the inner workings of the hiring process and the results are still not representative of the general population? Do you just assume you've somehow managed to eliminate all bias and the results must reflect the population? Or do you assume that you haven't managed to 100% debug this highly difficult process? It is very difficult to tell the difference between biased hiring and actual skew in the population. But when it's a trait that has no obvious connection to the job, it seems best to assume the process is biased, unless really good evidence exists to demonstrate a connection. For example, let's say your interview process produces hires whose heights are substantially above average. If you're hiring for a basketball team then this would make perfect sense. If you're hiring Java programmers, it's highly likely that your interview process is screwed up. It's possible that the population of good Java programmers is unusually tall because of some biological factor, but this idea needs some major justification before you use it to make decisions. Obviously, the first thing to do is to examine your interview process and eliminate any source of height bias you can find. But what if you do that and you're still hiring an abnormally large number of tall people? You could take this as sufficient evidence that tall people are better at this, but that's putting an awful lot of confidence in your interview program. It's more likely that you just haven't found all the sources of tall bias. If that's the case, then pushing for more short hires will improve the overall quality of your hires because you won't be artificially excluding good short ones.

ThrustVectoring 9y ago
>It's possible that the population of good Java programmers is unusually tall because of some biological factor, but this idea needs some major justification before you use it to make decisions. But "we're somehow secretly and unknowingly discriminating in our hiring process" doesn't need such justification before using it to make decisions?
- mikeash 9y ago
  Correct, because bias is prevalent and almost impossible to avoid. For example: does any part of your interview involve a subjective evaluation? If so, do any of the evaluators know the candidates' names? If so, congratulations, your process is probably biased! http://www.politifact.com/punditfact/statements/2015/mar/15/jalen-ross/black-name-resume-50-percent-less-likely-get-respo/ http://www.politifact.com/punditfact/statements/2015/mar/15/... That's literally all it takes: some human judgment and some way to guess at the applicant's race. The discrimination can happen subconsciously. You and I would probably fall victim to it despite our best intentions. Since the manifesto we're discussing is mostly about gender, it's worth mentioning that the same thing happens there: http://m.pnas.org/content/109/41/16474.abstract http://m.pnas.org/content/109/41/16474.abstract Nobody knows how to screen job applicants for pure merit without bias. If it's a choice between "our process is imperfect just like everybody else's" and "we stumbled upon the perfect unbiased screening process and all bias in the output is due to inherent variation in the population," bet on the first one every time. It's about a million times more likely.