In case you haven\u2019t heard, there\u2019s a worker shortage in the United States. As the country recovers from the pandemic, companies are trying to bring their employees back into the workplace but are finding that many of those employees have resigned \u2013 a socalled \u201cGreat Resignation.\u201d<\/p>\n
There are many factors behind this worker shortage, but Noelle Chesley thinks there might be one going overlooked: The use of automated hiring systems to fill those open positions.<\/p>\n
Chesley is an associate professor of sociology at 51ÁÔÆæ and her research focuses on the intersection of technology, work, and family. She\u2019s noticed researchers becoming more and more concerned with automated hiring systems and how they might actually be filtering out qualified candidates. These systems include a variety of software, but people might be most familiar with platforms like Indeed, ZipRecruiter, or Monster that use algorithms and keyword screening to automatically sort and match job seekers with employers. Other systems are custom-made for companies and may include applicant tracking, resume screening and ranking, custom analytics, assessment tools, and even personality tests.<\/p>\n
\u201cIf these algorithms work, terrific. They scale up really, really quickly and can reach millions of people. That can work really well,\u201d Chesley said. \u201cBut the opposite is true, too. I\u2019m a lot more worried about the opposite case, which is that the algorithms actually aren\u2019t working that well and we\u2019re scaling them up without attending to what the consequences of that might be.\u201d<\/p>\n
Chesley has identified four possible problems with automated hiring systems, and also has some possible solutions that may address some of their potential harms.<\/p>\n
Before job seekers can apply, employers have to make sure their job ad actually reaches those looking for work. But research has shown that using an automated system to do that can have some unexpected pitfalls, Chesley noted.<\/p>\n
\u201cThe algorithms in Google or LinkedIn, or several other platforms, have been tested on how that information gets sent out to different types of users. One of the things we know is that it doesn\u2019t work the same way for all users,\u201d Chesley said.<\/p>\n
The machine learning and programming for these algorithms can often draw conclusions that the programmers never intended. For instance, one audit of Google showed that, due to privacy settings, men were more likely to be shown certain job ads than women. An algorithm at Amazon searching for internal candidates to fill higher-up positions targeted only male candidates \u2013 because male candidates had been overwhelmingly hired for those positions in the past.<\/p>\n
\u201cYou might think that qualified women or qualified men might be equally likely to view that job ad,\u201d Chesley said. \u201cThe fact of the matter is they\u2019re not. The algorithms work in such black box ways that, depending on how people set their settings on their profile, for instance, can influence whether or not they\u2019ll even see the ad.<\/p>\n
\u201cTo the extent that, you know, women are half of the labor force, you\u2019re shrinking your applicant pool. There\u2019s the connection to the worker shortage,\u201d Chesley added.<\/p>\n
Estimates are that 99% of Fortune 500 companies use automated hiring systems in some capacity. Over the last two years, many more companies have turned to automation in their human resources departments, thanks to the shift towards online and remote work during the pandemic. While automated systems are necessary to wade through the vast pool of applications that companies receive, Chesley worries that their filters may be disqualifying perfectly good candidates \u2013 and they\u2019re doing it across the board.<\/p>\n
\u201cPart of it is the same algorithm issue with the job (ads), which is that there can be things that happen in these algorithms that lead to sort of weird outcomes,\u201d she noted. \u201cThe other issue is also that there some human tendencies that are getting reapplied and these algorithms are setting some really key filters.\u201d<\/p>\n
For instance, the algorithms could be told to screen out candidates who have more than a six-month gap in employment on their resume. That\u2019s a bad idea when society is trying to emerge from a pandemic in which thousands of people lost their jobs, Chesley said.<\/p>\n
And because these automated systems are so ubiquitous, applying those criteria broadly has a tremendous affect on the labor market.<\/p>\n
\u201cIf you\u2019re rejecting automatically with no human discretion, you might not even realize that your system is set up to do that,\u201d Chesley said. \u201cThat\u2019s the idea of rejecting qualified applicants at scale.\u201d<\/p>\n
In recruiter parlance, a \u2018purple squirrel\u2019 is a candidate with superior skills, that meets the qualifications and goes beyond them, the kind a recruiter would dream of hiring. In other words, a candidate as rare as a \u2018purple squirrel.\u2019<\/p>\n
Chesley said that recruiters are searching for them more and more.<\/p>\n
\u201cBecause it\u2019s possible to filter and get so much information about all of (a candidate\u2019s) different attributes, we\u2019ve had a raising of expectations in hiring There\u2019s some research to show that job ads are becoming more and more complex,\u201d Chesley said. \u201cThere\u2019s this uptick in expectations on the part of hiring managers.\u201d<\/p>\n
For example, employers may want to hire someone fully proficient in the Adobe Suite, but the job only requires using Photoshop. That can eliminate many qualified candidates and could contribute to the labor shortage.<\/p>\n
Much of the \u201cGreat Resignation\u201d has been fueled by employees seeking better treatment and wages, but Chesley wonders if there\u2019s not an additional reason: Oftentimes, applying for jobs can feel like throwing your resume into a black hole.<\/p>\n
\u201cI wonder a lot about the role of a very alienating job-seeking experience that is, at least in part, being fueled by automation,\u201d Chesley said. \u201cPublic opinion research suggests a general distrust of automation in hiring, and some more target studies of job seekers suggest that potential workers find interacting with these systems alienating.\u201d<\/p>\n
She added that automated systems are designed with the employer as the client with little regard to applicants.<\/p>\n
\u201cTo me, one of the things that is really unjust … is that you\u2019re actually contributing free labor and information that\u2019s going to be used by systems (like LinkedIn or Indeed) whether or not you get a job, so they profit from your information,\u201d she said.<\/p>\n
\u201cIf they know that they\u2019re launching ads on certain platforms, (companies) should have some of their technical people look and do a bit of testing to see who\u2019s seeing these ads,\u201d Chesley said. The same holds true for the internal systems that companies use to screen resumes and rank candidates.<\/p>\n
\u201cLet\u2019s just make sure that we\u2019re not scaling up some things that are problematic,\u201d she added, referring to the inadvertent sexism, racism, and other potential discrimination that algorithms can inadvertently introduce into a candidate search. Because organizations have control over the platforms they use to distribute job ads and their own automated systems, this is a natural place to think about making changes.<\/p>\n
Research shows that how companies craft a job ad can impact the kind of candidate who applies. Using more masculine language in an ad can inadvertently discourage women from applying, Chesley said. Employers should also think about the qualifications they\u2019re requiring: Is that qualification truly necessary for a person\u2019s job performance, or is the hiring manager searching for a \u201cpurple squirrel\u201d?<\/p>\n
Developers of automated hiring systems should reconsider who their product is for.<\/p>\n
\u201cThe idea is to harness the current fascination with improving customer experiences online (sometimes called \u2018UX\u2019 research) and translate that to thinking of job seekers as \u2018customers,\u2019\u201d Chesley said.<\/p>\n
Because hiring organizations are the actual clients, any push for change would have to come from them \u2014 to call for a product that provides a better experience for job seekers.<\/p>\n
\u201cWhat would that experience entail? More transparency, better communication with the hiring organization, timely decision making, etc.,\u201d Chesley added.<\/p>\n
\u201cGovernment and other stake holders, such as non-profits that work with job seekers, may need to develop interventions and policies that directly support job seekers in learning how to best navigate automated hiring practices, as well as policies that better regulate the use of automated hiring tools,\u201d Chesley said.<\/p>\n