This article was a featured article from Wednesday 8th April 2020 – Monday 20th April 2020.
You’ve heard the trend – diversity is the new global mindset in recruiting this year.
73% of companies embraced diversity, while over half of companies are already tackling it head-on to eliminate recruitment bias, according to a recent survey by LinkedIn.
But very few companies have cracked the diversity code – unconscious bias gets in the way which is hard to overcome due to its instinctive, fast and effortless nature.
The payoff for increasing diversity is a stronger culture, better performance, and deeper customer insight – more than 60% of companies surveyed agree with me on this one.
So can AI help us focus on our true intentions beyond recruitment bias?
AI assesses candidate data objectively – it’s unfamiliar with your assumptions and recruitment bias and critically, when implemented well, the results can be tested and validated.
Moreover, AI can be programmed to ignore candidate demographic information like gender, race, age – even zip codes that correlate with socioeconomic status.
A well thought out implementation of AI that includes blind results, learning from human decisions and balancing against a well-defined control can give excellent consistently fair results.
People are far from perfect, just like our hiring processes.
Of course, you might warn that AI algorithms are only as unbiased as the people who programmed them. Potentially, but fighting to address recruitment bias during hiring is a worthwhile effort, it is certainly better than taking no action at all.
People control their workplace culture. The weight of responsibility will remain with the hiring manager to create an environment that embraces diverse hires. AI may provide you with a shortlist of perfectly selected diverse candidates, you have to make sure they get a fair chance.
We’re building eva.ai with that in mind.
What do you do to ensure diversity in the workplace and eliminate recruitment bias?
The Business Transformation Network has shared this article in partnership with EVA.AI.
