[duncan_bayne]

The issue comes when you try to measure inclusiveness.

"We are indifferent to gender during our recruitment process."

"Okay, sure. How do you know?"

... at which point you need to know (amongst many other things) the genders of those applying, and those succeeding.

To be clear: I'm morally opposed to any sort of irrational discrimination in hiring. I define that as discrimination on any attributes irrelevant to the job, whether it's in favour of historically disadvantaged groups or not.

But if you want to be inclusive, you need to know whether you're actually being inclusive, and that requires metrics.

[sintaxi]

Lets say it could be measured accurately. What outcome would show a company is adequately inclusive? 50/50 male/female within management and non-management roles? What if that is not the ratio of qualified applicants or applicants in general? That doesn't even factor in trans people or non-binary genders. The situation gets infinitely more complex when race is involved as race is difficult to determine especially when people have ancestors of mixed backgrounds as almost everyone does. Should race be measured by ancestry via DNA test or by some sort of skin tone scale? How granular should we be with categorizing blacks, asians, or whites, etc or are the categories kept very broad? If so, why? Is height and weight also a factor? How granular is granular enough and who decides this?

What if someone self-identifies as a race or gender different than their appearance implies? Does someone decide how people are categorized or is it 100% embraced how someone chooses to identify themselves? Having one or two pie graphs seems incredibly dubious to me given the complex nature of this topic and major problems can arise from letting identity politics run wild.

It seems like a much better idea to me to evaluate applicants/employees blindly as much as humanly possible and to have a zero-tolerance policy to do otherwise.

[duncan_bayne]

So, let me further qualify this by saying I'm allergic to identity politics, and am waaaaay over on the Objectivist/Libertarian end of the political spectrum.

But I think you're throwing the baby out with the bathwater here.

Let's take a concrete example: at several large tech. companies, female engineering grads were being paid less than males with equivalent qualifications and skills. Yes, the numbers were aggregated across hundreds (maybe thousands?) of hires and there were most definitely individual exceptions in both directions.

But the overall trend highlighted a problem, which boiled down to a gender difference (again, w/ the 'aggregate statistics' caveat) between negotiating styles when it came to salary. Correcting the approach taken by HR smoothed out the difference over the course of years.

How would that have been noticed, investigated, and addressed w/o having metrics in the first place?

Edited: "It seems like a much better idea to me to evaluate applicants/employees blindly as much as humanly possible and to have a zero-tolerance policy to do otherwise." ... and I agree entirely. The purpose of the metrics is to learn whether you're doing an adequate job of that, _not_ to enable you to treat candidates differently based on their identity.

[sintaxi]

Thanks for the clarification though I'm still a bit fuzzy on how this works, perhaps you can unpack it for me.

Using your example, lets say there are 100 positions and 1000 applicants but only 35% of the applicants are female. Is your company inclusive if you hire 50 females or is your company inclusive if you hire 35 females?

If the answer is 50 females would it not stand to reason that those 50 females would earn less than the 50 males because they were the best 50 out of a pool of 350 competitors and the 50 males were the best candidate out of a pool of 650 competitors?

Or would the goal be to hire the best 100 and that should end up being roughly 35 females that are equally qualified and therefore earn the same?

Hope that makes sense.

[duncan_bayne]

> Or would the goal be to hire the best 100 and that should end up being roughly 35 females that are equally qualified and therefore earn the same?

Yes, that - hiring the best 100, and using the same criteria, and paying the same, regardless of gender.

If you _didn't_ wind up with 35 females in your scenario, then investigate. It might be that by chance few of the female candidates were good, but it might also be that they were penalised by inadvertent (or overt, in pathological cases!) irrational discrimintation.

Edited: oh, and often the systemic problems start earlier in the pipeline. Even the number of female applicants can be inadvertently driven down by creating job postings that appeal - in the aggregate, on average - more to men than women.

[Sprocklem]

> Or would the goal be to hire the best 100 and that should end up being roughly 35 females that are equally qualified and therefore earn the same?

In my opinion, this would make the most sense. You shouldn't be hiring specific portions of people. If, however, you have hired a bunch of people and you find that the number of women is disproportionately low or that their pay is unexpectedly lower, you should look into why that might be.

There are very few (if any) jobs where someone's gender (or race, etc.) affects their qualifications, except insofar as different proportions of each group enter the field.