MIT researchers have developed a new approach for using algorithms in the recruiting process that can help companies draw talent from a more diverse pool of job applicants. The approach yields more than three times as many Black and Hispanic candidates than companies may have considered using traditional resume screening algorithms. The algorithm also generates a set of interviewees that is more likely to receive and accept a job offer, which can help companies streamline the hiring process. A new working paper, “Hiring as Exploration,” details the results.
Firms are increasingly turning to algorithms to help them make hiring decisions. Algorithms hold the promise of saving firms time — they can process thousands of applications much faster than a human recruiter could — and also potentially improving screening decisions by unearthing predictors of applicant performance that humans might miss.
Traditional hiring algorithms look for characteristics of a job applicant that predict future success, based on a historical training dataset of applicants who have been interviewed or hired in the past. This type of approach, known as supervised learning, works well when firms have a lot of data on past applicants, and when the qualities that predict past success continue to predict future success. Yet there are many instances when both these assumptions may not be true. For example, applicants from non-traditional backgrounds may be under-represented in the training dataset, making it more difficult for firms to accurately predict their performance. Moreover, skill demands may change over time: firms hiring workers in 2020 may place more emphasis on an employee’s ability to work effectively in a remote setting.
“Static supervised learning approaches may push firms to replicate what has been successful in the past, and that may reduce opportunities for people with non-traditional backgrounds,” said MIT Sloan associate professor who conducted the research with PhD candidate Lindsey R. Raymond and Columbia University associate professor Peter Bergman. “You need to treat hiring as a dynamic learning problem in order to learn more about the quality of candidates you know less about, in order to make better hiring decisions in the future.”
Diversity matters, but firms struggle to improve
Some research has shown that more diverse teams are better for business. When leadership teams come from a diverse background — not just gender and race, but career path and education — companies report greater likelihood of innovation (according to a Boston Consulting Group survey), higher-than-average earnings (a McKinsey & Company study concluded), and a boost to their stock prices (according to Stanford University research).
In addition, a Weber Shandwick survey found that younger workers are increasingly likely to consider diversity and inclusiveness as an important factor in their job search. This suggests that firms that aren’t committed to improving diversity may have trouble attracting top talent, the survey noted.
Attempts to increase diversity in hiring face challenges when firms use traditional machine learning techniques to screen job applicants. Those algorithms focus on selecting the best workers based on what the firm knows right now, rather than considering the possibility that this may lead it to pass over qualified applicants from non-traditional backgrounds who are under-represented in its historical data. Li and coauthors believe that firms should strike a better balance between hiring from groups with proven track records and taking a chance on applicants from less well-represented groups, in order to learn about their abilities.
“Machine learning is increasingly being used to guide decision making — for credit scores, for who receives medical care, and for who gets hired,” Li said. “We were interested in looking at the way algorithm design impacts access to opportunity.”
Dynamic models find applicants who stand out
To address this, Li looked at hiring as a dynamic learning problem, analyzing applicants based on their upside potential or option value. The team’s algorithm assigns what it called an “exploration bonus” to identify candidates whose quality the firm knows the least about (given the firm’s existing data). These candidates might be rare based on their educational background, work history, or demographics, but they all share one thing in common: because the firm knows so little about them, it stands to learn the most from giving them a chance. This is referred to as “hiring as exploration” since, as Li put it, “you never know if you don’t try.”
Li, Raymond, and Bergman then applied the dynamic learning algorithm to a data sample of nearly 90,000 job applications that a single Fortune 500 company received over a span of 40 months. Researchers compared their output (the “exploration-oriented model”) to the output of two types of static learning algorithms — one that never changed and one that was updated after a round of 100 applicants (the “supervised learning models”) — and to the firm’s ultimate interview and hiring decisions. The firm was very selective; it rejected roughly 95% of candidates based on its initial resume screen, and only 10% of the candidates who passed the screening accepted a job offer.
Under an exploration-based algorithm, 25% of candidates selected for an interview were hired, up from 10% using human recruiters.
Using the supervised learning models, approximately 2% of the applicants who passed the initial resume screening were Black, and less than 5% were Hispanic. Under the exploration-oriented model, the shares rose to 14% and 10%, respectively.
“It’s not because the algorithms are looking to find them, but because the candidates are more rare and the algorithms are exploring them more,” Li said, noting that the algorithm is only designed to maximize quality, without any preference for gender or ethnic diversity.
“Companies and recruiters don’t have to say anything about their preference on demographics or work history,” she said. “The algorithms provide a hands-off way to decide what kinds of diversity to explore.”
The dynamic learning model demonstrated another benefit. The hiring rate among the candidates selected by the algorithms was 25%, compared to a 10% rate for candidates selected by human recruiters. Such a result would enable firms to schedule fewer interviews to fill a position. Li said it could also steer a firm away from continuing to select a large pool of job candidates unlikely to accept a role because they have competing job offers on the table — such as the practice of recruiting MBA graduates from high-profile programs for consulting or financial services roles.
“No one thinks that there aren’t talented people outside the Ivy League. The question is, how do we find them?” Li said. “We created a tool that allows us to identify people from groups that may have been traditionally neglected.”