Hiring bias can be detrimental, and the company may not even know it’s taking place. How can we use our technology and knowledge today to combat it?
AI-based tools have emerged as one of the most important technological advancements in recruiting, ever. AI and machine learning tools have been applied to automation(s) to help businesses discover top-tier talent.
It has also aided jobseekers by presenting them with the most suitable job opportunities. AI innovations have saved the recruiting industry countless man-hours, even in its early stages of development. But some challenges are coming to light as it becomes more common and widely-publicized.
AI tools, if not continuously optimized and monitored, can show patterns of hiring bias.
In this article, we are going to examine what hiring bias is and what innovations and strategies can be used to oppose hiring bias patterns from recurring in AI technologies.
What is Hiring Bias?
So, what are we talking about when we’re talking about hiring bias?
When we are talking about hiring bias in its most original form, it’s the tendency for the hiring agent/manager to focus only on the qualities of the job candidate that align with their personal pre-established opinions/preferences — whether they mean to or not.
“Biases can have an impact on recruitment, mentoring and promotions,” writes Pragya Agarwal, author of Sway: Unraveling Unconscious Bias. “This can hamper equal opportunities for women in terms of selection and progression to a high-level management and leadership role.”
For example, a recruiter may consciously or unconsciously hire a candidate on an unfair bases. Maybe they look like them, they are from their neighborhood, or the candidate went to the same school the employer did. Classifications like age, race and gender can be associated with certain jobs.
“If we don’t see male kindergarten teachers or female engineers we don’t naturally associate women and men with those jobs, and we apply different standards,” says Iris Bohnet, director of the Women and Public Policy Program, Harvard Kennedy School.
Basing a hiring decision on these factors is a bias towards the recruiter’s personal preferences. Hiring in this manner doesn’t guarantee that opportunity was shared by the most appropriate candidates for the role.
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How AI-based Algorithms Can Introduce Hiring Bias
Recruiting tools, ad platforms, and job boards can inadvertently introduce hiring biases into their candidate sourcing process. When soliciting for an open position, an ad platform will try and show an open position to the most relevant candidates. This doesn’t mean that the best candidates have visibility of the ad. But, it does mean that the algorithm is looking for the candidate that is most likely to click the ad.
There’s a big issue that can present itself with some algorithms powering these tools. They can adopt patterns of hiring biases directly from the recruiters they are trying to aid.
Personalized job boards like ZipRecruiter want to learn recruiters’ hiring preferences and use that data to predict and source new candidates based on previous hiring successes.
The problem here is that the hiring biases of the human recruiter are amplified by the speed and redundancy of the AI-based prediction algorithm. No effort is made to eliminate the hiring biases. The workforce can then end up lacking diversity.
The Harvard Business Review conducted a joint study with Northeastern University and USC, and they found that Facebook’s target ads showed a high-likelihood of sourcing candidates for ad visibility based on racial and sexual biases.
Broadly targeted ads for supermarket cashier positions were shown to an audience that was 85% women, and jobs for cab drivers were shown to audiences that were 75% black.
Likewise, Amazon was publicly criticized in 2018 when it was revealed that their AI resume scanner acted biased against women.
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How To Reduce Hiring Bias in AI-powered Recruiting Tools
AI algorithms will do is tell you where your biases are
– Shervin Khodabandeh, Boston Consulting Group
So, what lessons can businesses and AI tool developers learn from these instances? It’s fair to say that at this point, AI-powered recruiting/hiring tools are not “set it and forget it” solutions.
These powerful tools need to be consistently monitored, optimized, and re-aligned during their deployment by individuals who understand the goals of the organization.
Shervin Khodabandeh, co-leader of Boston Consulting Group’s AI business in North America, suggests that AI-based recruiting tools can avoid unforeseen hiring biases not by trying to predict who is the best candidate for the future, but by examining what hires have tended to be most successful in the past.
“One of the things good AI algorithms will do is tell you where your biases are. You’ll look at it and realize, I’m already favoring certain attributes unjustly and irresponsibly or unethically without even knowing it,” said Khodabandeh.
“This is where a human needs to come in and actively remove those biases and make decisions about which attributes to look out for in the next round of recruiting and hiring.”
By retroactively looking at successful hires, the positive patterns emerge that AI algorithms would not have ever recognized in the first place.
Jobiak and AI
AI is here to stay — in recruiting practices and countless other industries. The power of data reconciliation and analysis is needed everywhere. And, like any tool, it needs to be maintained, refined and used responsibly.
AI was never going to be a tool that was going to integrate and operate fully autonomously without any oversight. That type of technology is fictional and better left for movies and TV. AI-based tools need responsible decision-makers monitoring them.
Our goal is to use the power of AI to get MORE jobs discovered by MORE jobseekers. This gives hopeful candidates more opportunities to find work by giving job posts better visibility.
With Jobiak integration, open positions can be seen by talented and diverse candidates. We support businesses having fair hiring practices and an inclusive and diverse workforce.