How could they be less racist if they know the race than if they don't?
For instance, if you see that applicants that are otherwise equally qualified are getting rejected on recruiter screen disproportionally by race, you may have an issue with how your screening is done. And yes, this happens: https://eml.berkeley.edu//~crwalters/papers/randres.pdf
A reasonable goal is that race, gender, and sexual orientation should have no bearing at all on how likely it is that you get hired, promoted, or fired. So these inputs are typically blinded to hiring managers but available to HR in aggregate to let them perform these kinds of analyses.
I'll acknowledge this approach leads to some very odd interactions, like my school district asking what the sexual orientation of my five year old is. But it's not clear how else one can build a credible gauge for measuring and eliminating *isms from a system. (Open to hearing ideas!)
There being more male construction workers doesn't make it a sexist system.
There being more female teachers doesn't make it a sexist system.
There being more asian doctors doesn't make it a racist system.
Let people do what they want to do, you can look at demographics but stop trying to read racist tea leaves with it.
If you also collect demographics of applicants, it may tell a different story.
If your employee demographics look skewed, but they're consistent with applicant demographics, you have an easy out when accused of ism.
Of course, if you collect the information, you might also use it for ism purposes. Or you might just lose or discourage applications from non-favored people. It's hard to show.
(It also doesn't help that the federal categories feel poorly chosen and ill-defined)
Are you sure? There being more male programmers is widely used as evidence that it's a sexist system.
If females don't want to go into programming then don't make them.
There are really good female programmers, but males tend to be more frequent.
We all have different traits that make us good at different things, that's the beautiful thing about humans.
But if there is something to address, address the pain points specifically and directly.
Don't try to guess a "correct" ratio and work backwards with affirmative action (denying one race to boost up another).
For all you know the current ratio is the realistic ratio and where we should be.
In reality, the proportion of women in STEM directly matches the proportion of women in STEM majors, which directly matches proportion of women who say they're interested in STEM.
If it doesn't, you have an issue. The only way to know for sure is to measure.
E: To be clear, assuming equally qualified candidates, you would want expect hiring proportions to match the same A:B ratio as you get from applications.
No, I wouldn't expect this at all as I do not expect the height distribution of basketball player to reflect the height distribution of the general population. As I do not expect the personality trait distribution of pop stars to be the same of programmers, and again I do not expect the latter to be similar to the general population. Personal inclination, innate intelligence, talent, conscientiousness, and of course demographics, parenting, generational wealth all play a role.
Skin color is not an advantage when programming. It is also a characteristic by which it is illegal to hire a programmer.
That can be measured (imperfectly, but well enough), by filtering only for qualified candidates and then comparing the rate at which both A and B are hired and the rate at which they appear in the filtered list. E: This of course requires the filtering to be done _only_ with knowledge of a candidates skills/accomplishments, and association with demographics (including name, location, etc.) to be done only after the sorting.
Maybe institutions can afford to leave sexual orientation out of it until you’re talking about a body of people that is firmly within the age of consent threshold or has at least hit puberty. A lawyer might disagree but for him the remedy is to bend him over and remove the stick from his ass.
Some people think that affirmative action isn't racist.
But tell me what it is when you think like: "hey we have enough asians, exclude that person, let's find a black to get our quota"
Some people will defend it like a user said below:
"By engaging in deliberate anti-racism efforts to counteract subconscious racism."
But what is "anti-racism"? It's being racist in the opposite direction. You think Y group is being oppressed "subconciously" so you oppress X group as much as you think you need to, to "even" it out.
We should strive for equal opportunities, not equal outcomes.
Not measuring something doesn't make it go away.
Org A) 10% of applicants are brown, 9% of hires are brown
Org B) 10% of applicants are brown, 0% of hires are brown
Org A might be close enough to a rounding error you can call it good. Org B likely has some issues.
Hard to fix what you don't measure.
> Org A) 10% of applicants are brown, 9% of hires are brown
That means nearly all brown applicants for got hired. You're probably thinking this is a "good stat" and therefore a "good employer". But what if the rest of that company's stats were "90% of applicants are white, 80% of hires are white." Is that still a "good stat/good company"? According to you, it should be (and I would tend to agree.) In both cases, we're pretty much showing "If you apply for a job here, you have a ~90% chance of getting in, regardless of skin color." Not bad!!
But wait. What if the demographic of applicants is more like 50% white and 50% brown. (Assume a non-remote workplace). Now, one has to wonder why, in a demographic where half the population is brown, why only 10% are applying to this organization? Could be many reasons. Culturally, maybe the brown folds simply choose not to apply here. Or maybe the organization has a history of mistreating brown folks, abusing them, paying them less, etc. That could explain the low rate of applicants here. Your good example of a "less racist" org doesn't look so good anymore. Many factors could be a play; some less intentionally-evil than others. Hard to draw conclusions simply by reporting on one stat in isolation.
> Hard to fix what you don't measure.
Yes, but harder to think critically about the data you do gather.
Nope, the denominator is different. If you select the same percentage from each category the proportions stay the same.
If there are 100 fruits where 10 are apples and 90 are pears and each fruit has 10% chance to be selected then you get on average 1 apple and 9 pears => 10% of the selected fruits are apples.
"So which report makes an organization less racist"
So that implies that org B is more racist(because it hired zero brown people) - is that not the conclusion the author of the comment wants us to reach?
Forrest Gump is smarter than a rock, but he is not smart.
Do you or do you not think it is incredibly likely this is an unfair die.
That's Company B.
But hiring isn't random. Maybe all the "brown" candidates lacked necessary qualifications. Or needed visas which they couldn't get. Or a million other reasons other than "org B is racist".
My point is: you need further context. To look at that one stat as given above and conclude org B is racist is....unwise.
I am not sure I have ever been on a team without some kind of like-me bias. It's real hard.
Or in the exact time slice you got the data there weren't any brown applicants.
Ideally the race information you input would only be given to HR and not shown to anyone involved in the hiring process. It would then be used to identify anomalies indicative of bias occurring.
[1] https://www.wbur.org/hereandnow/2021/08/18/name-discriminati...
I'm just suggesting we let this same process unfold naturally. And that affirmative action, by placing emphasis on superficial differences between people, takes us in the wrong direction.
I don't doubt that some companies use this data to drive quotas but that isn't something inherent to collecting and analyzing race/gender info in hiring.
On top of that, something being impolite to mention doesn't mean it's not driving discrimination.
You seem to be avoiding the question. This process clearly works, and doesn't require any metrics collection or monitoring. Furthermore, DEI initiatives are clearly harmful to organizational goals[1] and clearly disenfranchise people who are just interested in colour-blindly carrying on with their work. Why do we need them?
PS. I'm hoping to preempt a no true Scotsman style reply about DEI. The example below was undertaken at a major corporation with the world-class consultants.
1. https://www.cspicenter.com/p/what-diversity-and-inclusion-me...
Across what time scale? Exactly how long is acceptable to you to wait as things work themselves out? Surely 400 years would have been enough time for this to really kick in.
> PS. I'm hoping to preempt a no true Scotsman style reply about DEI
I'm not defending DEI as practiced, I'm merely defending the idea that 1) racial discrimination in hiring happens, and 2) it's possible to do stuff about it faster than letting this work out "naturally".
I'd say ~80 years is about an appropriate amount of time. It's about how long it took antisemitism, anti-Irish and anti-Italian sentiment to die out. No amount of metrics will change the minds of adults who grew up in "a different time". Your only option is to wait for them to die.
> it's possible to do stuff about it faster
My point is that by attempting to address the concern faster than "naturally" you are almost certainly prolonging the "natural" time actual integration takes.
To answer your question - if there is such a complaint made, I'd appoint another qualified hiring manager(or HR person) to sit in on any future interviews with this person and give me their report on the situation.
Tl;dr: it is less about the company being less racist on their own by knowing the race, it is more about the company having to report those numbers so that others could hold them accountable (in case there are any arising concerns about racism).
And I consider almost all "anti-rqcism" to just be racism but against groups it's permitted to.
For example, asians and higher ed.