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This is Recruiting – Demystifying bias in recruiting and how to tackle it.

Reducing Bias in recruitment

This is Recruiting: Episode 4 – Reducing Bias in the Hiring Cycle with David Windley. 

Welcome to another interesting episode of “This is Recruiting”, a series that equips HR professionals and tech recruiters across the globe to gain actionable insights from fellow recruiters to take their hiring to the next level. 

In this episode, we caught up with somebody special, someone with a gold mine of useful information regarding technical recruitment. David Windley, CEO, IQTalent Partners, who is also Board Chair for the Society for Human Resource Management (SHRM) shares with us a generation’s worth of recruiting wisdom and valuable insights that he’s picked up over the decades. Having spent around 30 years in corporate HR, David is one of the leading industry experts in the world of recruitment. From all his years of observing, dealing with, and building processes around bias in hiring, he has much to say and offers us timeless advice on some of the best ways to tackle it.

The first step is always to call it out, he says. It begins with acknowledging that bias exists, then by rooting out the bad biases that aren’t performance-driven out of the process, and lastly by building workable systems around that. 

He maintains that the only way to overcome bias is by having recruiters zero back to the original principle of assessing the individual based on merits alone, remembering that they need to have the best interests of the broader organization in mind, and not give in to their personal inhibitions and prejudices. 

This is Recruiting – Reducing bias in the hiring cycle.

Sachin:

In your opinion, how important is it for an organization to focus on reducing bias while hiring? 

David:

So, let’s set aside the social issues. There are reasons to do it because of the broader social good. But let’s just talk as a business.

Our goal when we’re trying to hire people is to really find the right people that will be the best performers in our organisation – as an individual and collectively within our culture and company. So, when we’re trying to find the right characteristics that will lead to good performance and when we have bias creep in here – it’s only going to hinder our process of finding the ideal candidate for the position. 

Bias that’s unrelated directly to performance will only cause you to sub-optimize in your decisions. From a pure business perspective – all of us should want to address this issue. 

Sachin:

I’m sure you would have seen the length and breadth of different organizations and the functions within. In your experience, do you see certain functions that tend to be more diverse? Or the converse of that? 

David:

Yeah. Depending on how you talk about diversity. There is ethnic diversity, there is gender diversity, and then the broadest of all – diversity in thought and perspectives. But, yeah. If you just look at demographics and statistics, there are certain functions that lean more towards certain gender demographics and also ethnic demographics. So that’s true. 

Again, that doesn’t necessarily mean that we should just then assume — because at a macro level — those statistics are what they are. That means anything for any individual.  

So going back to the first question. A very good example of how bias creeps in is when someone looks at a macro and just makes an assumption based on ‘association by group’. But how much of those macro statistics have bias built into it is due to maybe reasons like bias in society, etc. So yes, at a macro level there are just historical differences in certain functions. The point is for any individual that you are assessing, you are trying to discern that person’s capabilities, skill sets, and competencies; whether they’re going to be a good performer and fit for your organization. 


The Go-Getter’s guide to diversity hiring in tech


Sachin:

Considering that humans are hardwired to align with people similar to themselves; affinity bias is so hardwired into us that it isn’t that easy to overcome. So, in such a situation what are your guiding principles that help you make the right decisions in the recruiting process? And what have you done with your team over the years? 

David:

Yes, I think you make a really good point. That’s where we start with this issue on bias – to understand that it is natural for humans to categorize. That’s just how our brains work. 

There is just so much information out there that we have to categorize things and it’s how we work. We need to just realize that bias is a natural thing and that we all have biases.

We all hear messages, we grow up in our societies, and whatever messages or things we learn or observe in those societies, they enter our unconscious and conscious mind. 

So, let’s first just demystify it. Bias exists. And the first thing to do about it is admitting that that’s the case. The issue now is to deal with the unrelated biases and to get that out of the process, so it doesn’t get in the way. 

Why do I say that? Since there are obviously some good biases too. For example, I have a bias for people that are self-starters. I think that’s an okay bias because it’s performance-related. But having a bias about someone’s gender, or someone’s ethnicity, or race is not directly related to those sorts of performance behaviors. So, from a process point of view, 

  • It’s good to have a structured interview assessment process that identifies the characteristics and competencies that you’re looking for.
  • Having structured questions around that and having a nice feedback loop as a team to make sure that when you’re assessing, you are, in fact, talking about those characteristics. 
  • Not relying on the shorthand – “Joe is a good guy. I like Joe.” That is not a good assessment. That doesn’t work. 

Want to keep going? Sachin and David go on to talk about centralized recruiting teams, the role of AI in reducing bias, hiring patterns and outlier statistics, diversity training, and more. 

Listen to our entire conversation with David here.

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