The 3 Biggest Misconceptions About Diversity
Tags: inclusion, hiring
Published: 2019-12-05 04:27:50

Diversity. The biggest topic in recruiting at the moment, companies all over the globe are scrambling to become thought leaders (*ugh*) on the subject by trying out new ways to make the traditional recruitment process more diverse. Strategies that first come to mind are things like a more meritocratic approach using an anonymous work sample submission and partnering with a 3rd party that recruits and trains diverse individuals. Here are the 3 biggest misconceptions about diversity.

## 1. The definition of diversity
> “Diversity is being invited to the party; Inclusion is being asked to dance.”

Diversity in corporate terms and in headlines is often defined by gender and/or race. But diversity is much wider than that. It can also be characterized by age, disabilities, diversity in thought, and much more. Diversity is looking into a crowd and not being able to describe a dominant “type” by the people in it. Similarly misunderstood, inclusion would be how integrated the crowd appears to be. Are people grouped up? Are these groups of the same type? Granted, this is a simplified example since not all definitions of diversity are easily visible. I’ll go into more detail on inclusion in a separate post.

## 2. AI

Quotas are also the poster child for AI (artificial inclusion) and can be a touchy subject. They’re like guilty pleasures — everyone has them but no one likes to talk about them. They serve as a great *starting point* in changing the default behaviors of common recruitment processes. It basically forces those involved to consciously acknowledge diversity. But at the same time, it can kill inclusion. Because the diversity feels more forced and surface level, there are many social dynamics that arise which cause some difficult situations.

### Quotas can undermine credibility

“Quotas” can help get people from underrepresented communities to get their foot in the door and they can also lead others to think less of them. Using my own experience as an example I can think of several times where I’ve been discredited of my own success because of my race. I’ve been told it’s only because of my race that I won a scholarship, or got the promotion, or landed the deal. Maybe you’ve heard noise from both sides of the affirmative action that has made [recent headlines](https://www.cnn.com/2018/10/14/politics/harvard-affirmative-action-asian-americans/index.html). I’ve been told being a minority is *easier*. It actually doesn’t make any sense to think of it that way, but I’ll reserve that rant for another time.

The point here is that the success of people from more diverse backgrounds is often undermined by others simply because they’re diverse. This is a lose-lose situation and doesn’t do much to help solve the original issue of diversity, or lack thereof it. It more boldly draws a line in the sand between two sides.

## 3. “We only hire the best talent, regardless of background”

Let me be the first to stop you right there because you’re already down the wrong path if you’re trying to promote a culture of diversity and inclusion. You may as well dim the lights, turn up the music, and throw a giant party for all the biases that are about to walk through the front door.

What defines “best” is up to the stakeholder(s) and that’s where a lot of bias creeps in no matter how unintentional it might be. Let’s use a Software Engineer as an example. When a Software Engineer is applying for jobs, it’s (*unfortunately*) common for them to have to complete some sort of coding challenge. A common coding challenge like, “write a simple class in the language of your choice to represent a deck of cards with operations to shuffle the deck and to deal one card” introduces a lot of bias.

Some might argue that you don’t need a computer science degree or a more traditional background to answer this question so the bias is mostly removed because anyone can answer it. If that’s the case, why are so many people who seem to do the “best” at this white males? That’s because this question and many like it are created by people from more traditional backgrounds and those from traditional backgrounds are better equipped to answer something like this.

Is it a true test of skill? Maybe…if you’re building a borderline fraudulent card game app for more pre-teens to get mindlessly addicted to while you pump ads through it as if your income depends on it — because it does.

Someone who doesn’t have any degree at all or has never even coded before could easily be a better learner and a smarter person, but the question isn’t set up to test for that. The question is a lay-up for those who have walked the path laid by the many fortunate people them. *It wouldn’t be a post without an obligatory sports reference, right?*

### Machine learning and artificial intelligence: quantifying bias

*I’m really on a roll with buzzwords. If someone created buzzword bingo, this post alone would lead someone to stand up and shout in victory.*

Talking about actual AI (artificial intelligence) now, let’s look at another case of bias. Amazon, you may have heard of them, has a reputation for [ruthless automation](https://www.fastcompany.com/90246028/how-ai-is-helping-amazon-become-a-trillion-dollar-company) and [looting cities across the country with its HQ2 lottery](https://twitter.com/dhh/status/1062381298084384768). Well, recently it’s been [reported](https://www.reuters.com/article/us-amazon-com-jobs-automation-insight/amazon-scraps-secret-ai-recruiting-tool-that-showed-bias-against-women-idUSKCN1MK08G) that Amazon applied machine learning to its hiring process to observe patterns in applicant’s resumes and output the best candidates for review.

So far it sounds great, right? Let’s take a moment to acknowledge that hiring and recruiting is hard, *hard*, **hard **work. Trying to help that process with today’s technology sounds like a real value-add and a business opportunity.

And as for the result?
> [“In effect, Amazon’s system taught itself that male candidates were preferable.”](https://www.reuters.com/article/us-amazon-com-jobs-automation-insight/amazon-scraps-secret-ai-recruiting-tool-that-showed-bias-against-women-idUSKCN1MK08G)

Similar to the hiring pattern that included little diversity in the past, the system taught itself to use the same biases that humans have been making. This really goes to show just how prominent these biases are. It’s eye-opening.

### “We don’t only hire white males, they just apply the most”

Right — I get it. If 93% of applications are white males then simple probability would suggest that the majority of the team would naturally look like that. The supply problem.

The issue here is recruiting tactics. When you recruit through traditional channels, you’re going to get traditional applicants. Different channels will yield different candidates. Far too to often do people recruit for diversity in traditional channels. An IVY league degree is a simplified example. Ah, but wait, [Harvard had it’s first “majority non-white” intake last year](https://www.bbc.com/news/world-us-canada-40812196). So some traditional channels have diversified ethnically, but remember that diversity isn’t only race and gender. There are many more examples of diversity, including socioeconomic status and disability.

Just to touch on the far too common white male example here one last time, when you recruit out of universities or channels that are predominantly white males you’re not going to get as many diverse applicants. Quality diverse candidates most often don’t take the same paths.

## The solution

Some real effort with a dash of self-awareness will go a long way. I know, I know — just ending with “do better” isn’t very helpful. But even just by making it this far, I hope diversity is even a half step closer to the top of your mind.

Another solution is to partner with organizations that specialize in diversity recruiting. Just to name a few that I’m very familiar with from my network:

* [Seize Every Opportunity](https://www.seo-usa.org/)

* [Venture for America](https://ventureforamerica.org/)

* [Code2040](http://www.code2040.org/)

Let me know what you think. I’d love to hear your thoughts as well!

### Why should you listen to me?

You shouldn’t. Educate yourself and make your *own* decisions 💯. I just like to try to improve on my writing by putting my thoughts down from time to time.