The term unconscious bias doesn’t mean being prejudiced when you’re asleep. Biases are the mental shortcuts we take every time we meet someone new, read the news or think about the future.
“Not me. I’m not biased at all!” you might think.
My sweet child. How spectacularly wrong you are.
Believing you aren’t biased is a form of bias. So the short answer to ‘how biased am I?’ is very.
There are over 180 cognitive biases. Design Technologist and Entrepreneur John Manoogian III put together The Cognitive Bias Codex to illustrate them all.
Print it out and slap it on your wall because these biases infiltrate your recruiting life as much as they do your everyday.
They’re necessary to survival. They help us process information, make sense of the world, act in the face of uncertainty and remember what’s important.
But biases also skew perceptions and affect decision making. And in recruitment, that can be the difference between making decisions that FEEL right in favour of those that ARE right.
The former will only get you by for so long. The latter means you’ll be more successful.
I’m not going to give you 180+ things to check yourself on. But simply being aware of the most common biases you’re likely to encounter is a good way to ensure you continue doing what’s right for your business.
Stereotyping’s often cited as one of the main barriers to diverse workplaces. The government’s been promising to crack down on it for years.
Believing generalisations about a group of people, and then applying those beliefs to individuals, makes no sense. But here’s what can happen if people aren’t thinking consciously.
One study, ‘Heidi vs Howard’, found that identical case studies elicit different feelings if the subject’s names are changed. Heidi was deemed to be unpleasant and selfish. Howard, powerful and revered.
Another study, ‘Are Emily and Greg More Employable Than Lakisha and Jamal?’, found that CVs with white names received 50% more interview requests than those with African-American names.
Stereotyping is a big, ugly problem. But when it comes to biases, it’s not the be all and end all. There’s often a lot more going on.
Potentially one cause of stereotyping bias, in-group favouritism refers to preferential treatment of people that resemble the subject. The most obvious examples being gender, race, religious belief, age and culture.
Social psychologist Henri Tajfel came up with Social Identity Theory and found that humans will categorise themselves into any old group, sometimes at random.
Being biased towards someone on these terms may stem from low self esteem. For example, managers hiring candidates that remind them of themselves feels like doing themself a favour.
This is when personal preferences overrule logic. We tell ourselves a person is inherently good if they have a handful of standout qualities, often in spite of evidence to the contrary.
The most common example of this is assuming that a well groomed, well dressed, physically attractive person possesses equally high standards in other areas of their lives.
It’s not just working harder for people you think look nice, it could mean overlooking a number of flaws in a candidate’s experience if they’re an angel in one or two other areas.
This is a broad term that defines failing to view the actions of individuals impartially. If a candidate goes quiet taking time to consider an offer, we might assume they’re:
– not interested (fundamental attribution error)
– not decisive (actor-observer asymmetry)
– trying to get a better deal elsewhere (hostile attribution bias).
“But 9 times out of 10 that’s all true”.
Yeah and other times it’s an illusion of transparency and good people lose out on good jobs.
I’ve written about shirking responsibility for negative outcomes before. This is that, but taking full credit for anything positive as well.
Deal fell through? “Well, the candidate had cold feet and the client was being difficult and a meteor landed on their Nan and then their dog died because of Brexit. Also GDPR”.
There’ll be any number of excuses that aren’t their fault. But made a placement? “All me”.
People affected by self serving bias are unlikely to recognise the consequences of their actions accurately. Long term, this can undercut resiliency and performance.
This affects you and your clients in slightly different ways. Predominantly when you’re getting your shortlist together and again when they review it.
You’ve gone through a few CVs and you’re happy with Option A. But then Option B drops into your inbox. And B stands for Banger. Option A looked pretty darn good by themselves, but next to Option B they look average.
It’s this comparison that can alter the value of your shortlist before you send it. And once your client receives it, they’ll be contrasting it against post-holders past and present as well as their other submissions.
Underpinning all of this is yet another bias: the illusion of control. This can make us feel more influential than we actually are.
Your candidates, managers and clients are just as affected by bias as you. And there isn’t a great deal you can do about them.
You CAN lead by example and manage yourself, however. Removing names and contact details from CVs is a start, but there’s a deeper level you can go in the quest for an unbiased recruitment landscape.
● Take your time – a luxury many recruiter’s can ill afford, but if it’s going to make the difference between a decision that FEELS right and one that IS right, it’s worth it
● Benchmark your shortlists – are you delivering the best candidates in the market or the most available? How do you know they’re good?
● Individualise your submissions – avoid comparing your shortlist against one another and make applications based on their own merit
● Go back to basics - return to the original brief periodically and ask yourself whether your candidates tick all the boxes instead of just one or two select ones
● Utilise a second set of eyes - loop in a colleague or your manager to sanity check your work and add another layer of impartiality
● Track your outcomes - did you really lose 9 out of 10 deals because of a flaky candidate or is there more at play here?
Would AI rec-tech eliminate bias or perpetuate it?
On one hand, AI isn’t beholden to human biases. On the other, if we’re programming Robocruiters to make data-led decisions, and to learn from those outcomes, their insight is limited by the sample they’re pulling from.
Isn’t that the same way we accrue our own biases, albeit at scale? Is machine learning subjective by design or can objectivity be hard wired? Could an algorithm recognise a purple unicorn?
We’re still a long way from that future but it doesn’t stop us speculating. In the meantime, if you’re on top of your own biases, that can only mean more impartial decision making.
I reckon that means more placements and more commission, although I could be biased.
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