IT Management

Diversity in Tech: Tackling Unconscious Bias

Published by in IT Management

Whenever I come across an article about the importance of diversity in tech, why you need diversity in tech, or why there’s a lack of diversity in tech, each post always seems to include the same two words: unconscious bias.

So what is it?


According to the Office of Diversity and Outreach at University of California, San Francisco, unconscious biases (also called implicit biases) are “social stereotypes about certain groups of people that individuals form outside their own conscious awareness.”

“But I’m not biased! My decisions are quantitative — I use performance appraisal software, applicant tracking software, and IT management software. I can’t fall victim to these problems!”

That’s what I thought, except it’s important to remember is the latter half of UCSF’s claim, too: these biases are outside of our conscious awareness. In other words, you’re not doing it on purpose. You’re doing it unconsciously.

“Everyone holds unconscious beliefs about various social and identity groups,” UC San Francisco continues, “and these biases stem from one’s tendency to organize social worlds by categorizing. Unconscious bias is far more prevalent than conscious prejudice and often incompatible with one’s conscious values.”

For workplace diversity, this concept can explain large gender, ethnic, and age disparities, and likely underlies why it takes so long to implement real, productive change. Because how do you know whether or not you’re contributing to the problem if you don’t even know you’re part of it?

So what’s the big deal about all of this?

Implicit biases can affect all aspects of the tech workplace, from team environment to the hiring process. If that’s not concerning enough, your company can also get sued, meaning lost money and a lost reputation.

But how can you tell if you exhibit these biases in the first place?

Let’s find out.

The Implicit Association Test (IAT)

Originally published in 1998, Tony Greenwald (University of Washington), Mahzarin Banaji (Harvard University), and Brian Nosek (University of Virginia) set out to find what they hypothesized to be a relationship between memory (both implicit and explicit) and social constructs.

Through certain tests, participants categorize or group words and images at rapid-fire succession, revealing their implicit biases, which may be vastly different from their conscious beliefs.

“Certain scenarios can activate unconscious attitudes and beliefs,” states UC San Francisco. “For example, biases may be more prevalent when multitasking or working under time pressure,” such as the time restraints placed on participants in this test.  

In their results, test takers can see if they potentially harbor these implicit biases (even slightly), which can be the first step in ridding yourself and your team of stereotypes that could be preventing a positive and inclusive work environment.

To create a more diverse, accepting tech environment, it’s important to confront the obstacles. It may be hard to admit your own inadequacies, but knowing if you have them can be the first step to understanding the problem and enacting change.

Below, I’ve listed three popular IATs that can help you identify potential biases that could be affecting your tech workplace so you can identify and rid your workplace of unconscious prejudices.

Gender – Science IAT

The gap between women and the world of tech is nothing new. Well, not exactly.

According to a 2013 study by the American Association of University Women, the outlook for women in tech isn’t optimistic. In fact, Emily Peck of the Huffington Post reports, “the percentage of computing jobs held by women has actually fallen over the past 23 years.”

This study implies that the lack of women in tech isn’t something that’s always been. It’s not even that women are trying to break into the industry as much as regaining lost footing.

In a recent report by McKinsey & Co. and LeanIn.Org, “women currently in tech feel pessimistic about the climate in their companies,” says the Wall Street Journal. Furthermore, “29.9% of female tech employees polled said they felt gender played a role in their missing a promotion or raise, and 37.1% of female tech employees said they felt their gender would disadvantage them in the future.”

With such a large number of women expressing dissatisfaction, these feelings could impact the number of women in the tech industry, potentially damaging the future of the industry as a whole.

By 2020, there will be 1.4 million jobs available in the computer sciences, says Reshma Saujani, the founder of Girls Who Code. Yet, qualified graduates will only fill 29% of these openings, with women making up less than 3% of hires. Such low numbers, and especially such a low turnout for women, could prevent the technology sector from reaching its full potential.

Already women make up 47% of the total U.S. workforce and “are projected to account for 51% of the increase in total labor force growth between 2008 and 2018.” By eliminating almost half of the workforce from the applicant pool, businesses (including yours) could be missing out on more than half of available talent.

Are you part of the problem?

If you’re interested in examining your implicit biases towards gender and science, IAT offers a Gender – Science test, which you can take here.

*If you want to see how your overall perception between women and careers, there’s also a more general test, Gender – Career, which may show your leanings towards the links between women and family as well as men and the workplace.

Race (‘Black – White’ IAT)

African Americans and Hispanics are vastly underrepresented in the technology community.

According to Google’s current diversity statistics, Hispanic employees make up just 3% of all tech jobs at the company, with black employees trailing at a dismal 1%. The numbers are improving (somewhat) with African Americans constituting 4% of all 2015 hires at the company and Hispanics at a close rate of 5%. However, these numbers don’t breakdown into workforce role, meaning you can’t see what fraction of these percentages went into tech and non-tech positions.

Despite transparency regarding the company’s gender and ethnic makeup, Google doesn’t show the cross sections of race and gender, leaving men and women of color unrepresented. If black employees only comprise 1% of total positions, it’s fair to assume that black women makeup less than 1% of Google’s workforce, an even more dismal statistic.

When talking about race and tech, many turn to what’s called the “pipeline problem,” essentially the notion that lacking minority representation is due to the scarcity of black and Latino/a students majoring in computer sciences and engineering (which is true, but still due to a host of factors like lack of confidence and outreach).

Even with low turnout, the stats on minority tech graduates don’t match overall employment numbers in the industry. For example, the New York Times cites data from the American Community Survey, which reports that “young computer science and engineering graduates with bachelor’s or advanced degrees” break down statistically into 57% white, 26% Asian, 8% Hispanic and 6% black. Compare those numbers with popular tech giants like Google, Microsoft, Facebook, and Twitter, which hover around “56% white, 37% Asian, 3% Hispanic and 1% black.”

While the difference isn’t monumental, these percentages still dip 5% or more for Hispanics and African Americans, something sociologist Maya Beasley tells the publication is likely due to an unwelcoming work environment.

Are you part of the problem?

To determine if racial bias may factor into your hiring or your team culture, you can take the Race (‘Black – White’ IAT), which tasks users with distinguishing between faces of European and African origin, testing whether or not you may have an unconscious preference for white over black.

A ‘Skin-tone’ IAT is also available to help workers examine if they have an unconscious preference for lighter skintones over darker ones.

Age (‘Young – Old’ IAT)

Prejudice towards older workers in tech isn’t discussed nearly as much, but it’s what some have called one of tech’s biggest (open) secrets.

Whether it’s the idea that older generations can’t navigate new technologies, can’t perform up to par with their younger counterparts, or even that they may lack the energy of someone younger, these are all stereotypes that dished out $78 million in discrimination suits from 2015 alone.

But isn’t this a problem across all industries?

True. Regardless of industry, there is an inherent biased against older workers, with ageism beginning as early as 35 (particularly for women), though the Age Discrimination in Employment Act (ADEA) is only effective for those aged 40 or older.

According to a survey by AARP, 64% of respondents have seen or experienced ageism in the workplace, with one in three older workers experiencing discrimination or seeing it firsthand.

And it’s a problem that’s on the rise. In 1997, continues AARP, more than 15,000 age discrimination complaints were filed to the EEOC. In 2014, that number swelled to more than 21,000.

But for tech, this problem grows substantially. The median age of the American workforce is just above 42-years-old, but Google’s average employee is just 29-years-old, a 13 year gap in the average.

Still not convinced?

Even Facebook’s Mark Zuckerberg has bought into the stereotype, stressing “the importance of being young and technical,” even outright stating at one time that “young people are just smarter.”

But the irony of ageism, says Greg Baumann, editor-in-chief of the Silicon Valley Business Journal, is that “eventually everyone becomes a potential victim. It’s the universal identity group for those who don’t die young.”

Are you part of the problem?

As a tech employee, it’s important to understand that a variety of perspectives can help to build a strong team. Someone older isn’t necessarily out of touch as much as has a different perspective on how to tackle projects and problems.

To find out whether you carry these implicit biases, take the IAT’s ‘Young – Old’ test here.


How did you do on the Implicit Association Test? Let me know in the comments below.

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