How do I know if a disparity in representation or my recruiting pipeline is a result of workplace discrimination and not based on known group differences?

The latest research and statistics suggest that STEM fields will remain overrepresented by Asian and White males, and not due to factors (like unlawful discrimination) in the workplace.

The Gender-Equality Paradox: An unintuitive finding from recent research on adolescent achievement in science, mathematics, and reading (N = 472,242) is that as a country becomes more gender-equal and cultural influences on gender decrease (women are less restricted by societal norms and get to choose the career they want), differences in sex (like interest in people vs things) are maximized (Stoet & Geary, 2018).
https://journals.sagepub.com/doi/abs/10.1177/0956797617741719

“Falk and Hermle studied 80,000 individuals in 76 countries who participated in a Global Preference Survey and compared the data with country-level variables such as gross domestic product and indices of gender inequality. They observed that the more that women have equal opportunities, the more they differ from men in their preferences.” (2018) https://science.sciencemag.org/content/362/6412/eaas9899

Group Differences in Mathematics Achievement: There is a known disparity in academic achievement between groups, especially in mathematics. The disparity in the percentages of college-bound, male 12th graders with an Advanced level of math is difficult to ignore – 17% of Asian males achieve Advanced mathematics competency, while the percentage of black males who did the same is so low that it rounds to 0% (.22%; Nations Report Card, 2015).
https://www.nationsreportcard.gov/ndecore/shareredirect?su=NDE&sb=MAT&gr=12&fr=3&yr=2015R3&sc=MWPCM&ju=NT&vr=SDRACE-false--GENDER-false--B0269F1-false&ct=SDRACE-GENDER-B0269F1&st=ALD-BB-BA-PR-AD&sht=OUTPUT&urls=xplore&sm=false&sj=false-NT&sy=2015R3&ss=ALD-BB-BA-PR-AD&chl=Data%20Chart%201&chgb=None|None|true|1&chv=SDRACE|Race/ethnicity%20used%20to%20report%20trends,%20school-reported|true|3&cht=BarChart&chs=YEAR|2015|2015R3--JURISDICTION|National|NT--GENDER|Male|1--B0269F1|Yes|1--ALD|at%20Advanced|AD&cut=DATACHART&opt=BAR

Hi @Tim,

I want to share a personal story before launching into why I think discussing “known group differences” is problematic and harmful. This might be a little hard to digest, but I hope you’ll be able to hear me out, because it’s such an important topic.

From an early age - I was told I was good at math and excelled in the subject in school. As an Asian male, I was determined to fit this positive stereotype and through intense internalized pressure, got a perfect score on the SAT math. Around this time, I was a volunteer math tutor for kids on the “other side” of town. This is a little uncomfortable for me to admit, but because I was young and influenced by stereotypes, I figured the black kids I was working with (ranging from 3rd grade to 10th grade) would not be as gifted as I was. I was dead wrong - the kids I tutored had the same ability to pick up the concepts as quickly and deeply as me. I learned through that experience, so much of what we attribute to “ability” or “preferences” is really about access and privilege. Maybe, these kids had been told that “known group differences” were the reason they weren’t as good as math as me and people who look like me. But we now know how harmful these stereotypes can be.

Perpetuating the myth that “known group differences” are the reason why women and minorities are underrepresented in talent pipelines is very harmful - it reminds me of the Google Manifesto argument from a few years back. Through reading about the Google Manifesto and many of the brilliant responses (I loved this one, and encourage you to check it out too: https://medium.com/@yonatanzunger/so-about-this-googlers-manifesto-1e3773ed1788), I learned that software engineering is actually all about cooperation, collaboration, and empathy for both colleagues and customers. The truly hard parts about the job are knowing which code to write, building the clear plan of what has to be done in order to achieve which goal, and building the consensus required to make that happen. Referencing “known group differences” in math, biological differences, or otherwise - is perpetuating the same myth. Social conditioning might play a part in why women don’t feel comfortable in software engineering roles. Women are socialized to be better at paying attention to people’s emotional needs and so on — this is something that makes them better engineers, not worse ones. That’s why it’s such a shame that they are discriminated against in the field.

Making an argument that “known group differences” are the reason for disparities in talent sends a message that some large fraction of your colleagues are at root not good enough to do their jobs, and that they’re only being kept in their jobs because of some political ideas. Trying to legitimize these ideas in a company setting causes significant harm to people in the company and the entire company’s ability to function. If somebody was to make this argument while employed at a company, it would be unconscionable for anybody to assign work to them. Even if placed in a group of like-minded individuals, nobody would be able to collaborate with them.

Opinions like this pose a lot of problems in the tech industry and others. These views are fundamentally toxic to any organization they show up in, so it’s important we exercise judgment and think carefully about the potential impact of our words.

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“I learned through that experience, so much of what we attribute to “ability” or “preferences” is really about access and privilege.”

I hear you, @Steven_Huang – it’s important to consider the influence of access and privilege and no one is saying these statistics should to used to justify discrimination or perpetuate stereotypes - any tool, used improperly, can be used for harm. That doesn’t mean a tool like that is inherently useless, wrong, or that it can’t be used responsibly.

Also, ability and preferences do exist (you couldn’t have achieved a perfect SAT math score with an IQ of 80) and they are more determined by biology and genetics than most people understand. D&I professionals are right to point out social influences of behavior, but they don’t equally consider biological influences. The point of my question was to ask how each should be considered, given an example. A decision based solely on social influence is one-sided given what we know about the influence of biology on behavior, and it’s difficult to make informed decisions when your only story is part of the story.

“Making an argument that “known group differences” are the reason for disparities in talent sends a message that some large fraction of your colleagues are at root not good enough to do their jobs, and that they’re only being kept in their jobs because of some political ideas.”

The idea that biology influences behavior is about the least politically-motivated idea there is (at least for those who understand the mechanisms).

I think there’s a way to talk about biological influences without perpetuating stereotypes. Perhaps you should consider the risk of not talking about their influence. They’re not going away and neither are the people on the far right who would intend to use these tools to promote their own racist and sexist views. Why not show them (and everyone else) how to use these tools correctly?

If you or anyone else needs a refresher on the biological influence of human behavior, I recommend starting here – https://www.youtube.com/playlist?list=PL150326949691B199

I’ll check out the youtube video and try to keep an open mind. It’s been a long time since I’ve taken a biology class. Curious if any other D&I folks on People Geek Answers want to chime in here. Is there a way to talk about biological influences in a way that explains disparities?