Risky behaviour: Experts warn of 'unintended consequences' of relying on word-of-mouth recommendations
Faced with a deluge of AI-generated job applications, many employers are reverting to the familiar comfort of the "hidden" job market – a hiring process based on word-of-mouth recommendations, personal referrals and other networks such as university alumni connections.
While artificial intelligence (AI) tools offer speed and scalability, they also introduce challenges for HR professionals: overwhelming applicant pools, duplicated resumes, and increased difficulty in screening effectively.
These conditions may be leading to renewed emphasis on informal hiring networks instead of job postings.
Hidden job markets: cost saving return to pre-DEI hiring
According to Statistics Canada, informal network hiring made up 72.5 per cent of hiring in the third quarter of 2024, not far behind online job board posting, the top mode of recruiting at 79.8 per cent.
However, too much emphasis on hidden job markets can be problematic for several reasons, according to some experts.
“We are seeing a turning back the clock in a way,” says Izumi Sakamoto, associate professor at the at the Factor-Inwentash Faculty of Social Work at the University of Toronto.
"The more lean organizations are, instead of going through tens of thousands of resumes, if you know of ‘somebody's friend’, that may feel like a risk-cutting measure, that would minimize the time for hiring, and you have assurance of ‘friends of friends’ … and that seems like a good idea for hiring managers.”
While the cost-saving appeal is understandable, Sakamoto warns of the longer-term consequences: undermining fair hiring practices.
Effects of hidden job network hiring on innovation
Vern Glaser, professor of strategy, entrepreneurship and management at the Alberta School of Business, notes that while hidden job markets aren’t new, they are becoming more important now with the advent of AI.
He points out the “unintended consequences” of relying too much on hidden job markets on a workforce’s diversity, and also its ability to innovate.
"If your source of employees is employee referrals ... typically, you'll have a homogeneous workforce because employee referrals are going to be coming from people who are similar to them. So you get similar backgrounds, education, social networks,” Glaser says.
“A lot of times what happens in these situations is you have underrepresented groups who are excluded … if you're going off your own networks, you can have reduced candidate diversity, so you can have a limited talent pool.”
Informal biases in soft skill assessments
When organizations depend on informal networks for hiring, it’s not just about who gets in, it’s also about how those hires are evaluated, says Sakamoto, explaining that referrals can sometimes bypass formal evaluation criteria.
This can create performance issues and garner resentment among other employees.
Hidden hires may be given an edge in assessments for soft skills, says Sakamoto, such as communication or cultural fit. When managers are familiar with a candidate’s personal network or background, they may unconsciously score them higher on these metrics; these informal advantages further entrench inequalities, leaving many high-potential candidates overlooked.
“If their communication styles are more in line with what you're used to, then they may get more points, as opposed to somebody who's [an] immigrant or trans persons ... so then, even if their communication may be as effective ... there's a risk of them getting lower points.”
Avoiding risks of hidden job markets
Beyond efficiency, there are deeper power dynamics at play when organizations hire from informal job networks. Sakamoto outlines a scenario that reflects implicit pressure within organizational hierarchies: “If I know that whether I can stay in this organization might have to do with whether I hire this director's son or daughter or friend ... I might do biased hiring myself,” she says, emphasizing that even the most well-meaning HR professional may have to yield to internal politics.
She urges organizations to guard against this by formalizing referral processes: “[Find] ways to evaluate referrals, and standardize referrals.”
For HR leaders looking to address these challenges while still leveraging the speed of referral hiring, Sakamoto offers clear steps:
- Conduct regular audits: “Auditing their own hiring, who's hired, looking at demographics, education, the schools they went to, where they live ... need to be looked at closely.”
- Track outcomes: “Monitor if people who are hired through referrals do better or worse or the same with the rest of the workforce."
- Do active expansion of job posting reach by looking outside of the regular platforms: “Active outreach to different places, job postings in different job boards and community networks, LGBTQ+ professionals or racialized professionals’ groups and ... immigrant organizations."
Using AI to improve hidden job market hiring practices
Glaser adds that companies can use technology thoughtfully to improve these processes, and to preventatively solve for homogenous hidden job market hiring: “My recommendation would be use AI creatively, view it as an opportunity.”
“If you're basically looking at candidate pools and you're concerned about some of these issues related to diversity … you can use AI as a creative coach in those types of situations, to also help you make sure that you're not doing that.”
He adds that AI technology can be used to help create processes and frameworks to guide hiring practices.
“A lot of times, the challenge with internal processes are that you might not follow those steps or those guidelines,” says Glaser.
“So, you can use AI to help you construct good, reasonable, defensible processes … large companies probably have these types of processes. But I think for particularly small and mid-sized companies, there's a lot of help that you can receive from using these new technological tools.”
