Comment by sfteus
2 months ago
My company's DEI program effectively does this. The main tenets are:
- Cast a wide recruiting net to attract a diverse candidate pool
- Don't collect demographic data on applications
- Separate the recruiting / interview process from the hiring committee
- The hiring committee only sees qualifications and interview results; all identifying info is stripped
- Our guardrail is the assumption that our hiring process is blind, and our workforce demographics should closely mirror general population demographics as a result
- If our demographics start to diverge, we re-eval our process to look for bias or see if we can do better at recruiting
The separation allows candidates to request special accommodations from the interview team if needed, without that being a factor to the committee making the final decision.
Overall, our workforce is much more skilled and diverse than anywhere else I've worked.
> Our guardrail is the assumption that our hiring process is blind, and our workforce demographics should closely mirror general population demographics as a result
> If our demographics start to diverge, we re-eval our process to look for bias or see if we can do better at recruiting
These are not good assumptions. 80% of pediatricians are women. Why would a hospital expect to hire 50% male pediatricians when only 20% of pediatricians are men? If you saw a hospital that had 50% male pediatricians, that means they're hiring male pediatricians at 4x the rate of women. That's pretty strong evidence that female candidates aren't being given equal employment opportunity.
A past company of mine had practices similar to yours. The way it achieved gender diversity representative of the general population in engineering roles (which were only ~20% women in the field) was by advancing women to interviews at rates much higher than men. The hiring committee didn't see candidates' demographics so this went unknown for quite some time. But the recruiters choosing which candidates to advance to interviewing did, and they used tools like census data on the gender distribution of names to ensure the desired distribution of candidates were interviewed. When the recruiters onboarding docs detailing all those demographic tools were leaked it caused a big kerfuffle, and demands for more transparency in the hiring pipeline.
I'd be very interested in what the demographic distribution of your applicants are, and how they compare against the candidates advanced to interviews.
Yea when I have done hiring the vast majority of applicants were of specific races and demographics. It isn’t a private companies’ job to skew hiring outcomes away from the demographics of the incoming pool of qualified applicants. If you have 95% female applicants for a position I would expect that roughly 95% of hires are going to be female and vice versa.
I think it is damaging when hiring outcomes are skewed as well as it undermines the credibility of those who got hired under easier conditions fabricated by the company.
I too agree with the grandparent post that we should try to be scrubbing PII from applications as much as possible. I do code interviews at BIGCO and for some reason recruiting sends me the applicants resume which is totally irrelevant to the code interview and offers more opportunities for biases to slip in (i.e this person went to MIT vs this person went to no name community college).
> If you have 95% female applicants for a position I would expect that roughly 95% of hires are going to be female and vice versa.
I would disagree for the most part. As mentioned above, there are roles where you'll see gender bias that may not be addressable. In the OB/GYN example, I understand some women would only be comfortable with a doctor that is also a woman. That's not necessarily addressable by shoe-horning in male doctors. But again, that can be accounted for in DEI programs.
It's also more understandable to non-remote jobs. Some areas have staggeringly different demographics that could only really be changed by relocating candidates, which isn't feasible for all business. Mentioning this specifically as my company is fully remote.
Otherwise, in my opinion, a candidate pool that is 95% some demographic shows a severe deficiency in the ability to attract candidates.
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> These are not good assumptions. 80% of pediatricians are women. Why would a hospital expect to hire 50% male pediatricians when only 20% of pediatricians are men? If you saw a hospital that had 50% male pediatricians, that means they're hiring male pediatricians at 4x the rate of women. That's pretty strong evidence that female candidates aren't being given equal employment opportunity.
We track these, but don't establish guardrails on that fine grained of data.
In your example, it would be balanced by a likely over-representation in urology by male doctors. But when looking at doctors overall, the demographics tend to balance out, with the understanding that various factors may affect specific practices.
To give you a more solid answer, in our data we see that men are a bit overrepresented in our platform engineering roles, while women are within our data science and ML roles. General backend/frontend roles are fairly balanced. Overall engineering metrics roughly fit out guardrails. We look at the same for management, leadership, sales, and customer support.
I don't have direct data on the recruitment -> interview process on hand. I work on the interviewing side though, and can tell you anecdotally that I've run dozens of interviews and overall haven't noticed a discrepancy in the candidates I've seen. I can also say that of those dozens, I think I've only advanced 2 candidates to the hiring committee. So we seem to err on sending a candidate to interview vs trying to prematurely prune the pool down.
> To give you a more solid answer, in our data we see that men are a bit overrepresented in our platform engineering roles, while women are within our data science and ML roles. General backend/frontend roles are fairly balanced. Overall engineering metrics roughly fit out guardrails. We look at the same for management, leadership, sales, and customer support.
So you have a slightly more than 50% women in data science, a field that's 15-20% women [1]. Likewise, software development is ~20% women. But your frontend and backend roles have 50/50 men and women. You're achieving results representative of the general population but you're obtaining a very large overrepresentation of women relative to their representation in the workforce. We're talking overrepresentation by a factor of four or five.
All of the fields you listed ~80% male. This isn't like a hospital that's equally comprised of urologists and OB/GYNs. It's like a hospital exclusively comprised of urologists, but somehow hires 50% women.
> I don't have direct data on the recruitment -> interview process on hand. I work on the interviewing side though, and can tell you anecdotally that I've run dozens of interviews and overall haven't noticed a discrepancy in the candidates I've seen.
Discrepancy is a relative statement. What is the gender breakdown of the candidates you've interviewed? Remember, if the software developers you're interviewing are 50/50 men and women, that is representative of the general population but it's a 4x overrepresentation of women relative to their representation in the field. If by "no discrepancy" you mean "no discrepancy relative to the general population" it sure sounds like female applicants have a much better shot at getting interviewed. If you're seeing 50 / 50 male and female interviewees in a field that's 80% male, you really ought to question whether recruiters are using gender as a factor in deciding which applicants to advance to interviews.
Is your company's goal to achieve representation equitable with respect to the general population, even if it means applicants from one gender are significantly disadvantaged in interviewing? Or is it to give equal employment opportunities to candidates, regardless of their gender? It sure sounds like your company is pursuing the former. I would highly suggest pushing for more transparency in the application to interview pipline if you care about gender equality.
1. https://www.bu.edu/articles/2024/women-in-computer-data-scie...
notice how these solution requires a dedication to diversity throughout the process from candidate sourcing to interviewing and all the way through, and not some simple cut and paste answers.
The road to a more inclusive solution is dedicated effort, with continuous re-assessment at every step. There is no magical answer.