Researchers have developed a new way for using algorithms, which can help recruiters draw talent from a more diverse pool of applicants.
Algorithms are popular
Algorithms are a popular way to improve the hiring decisions of firms, but they don’t always work that well. Although it’s said that they save firms time because they can process hundreds of applications much faster than a recruiter could, it also has a downside. Hiring algorithms use the information on workers they have previously hired in order to predict which job applicants they should now select. In many cases, relying on algorithms that predict future success based on past success will lead firms to favor applicants from groups that have traditionally been successful. If firms used their historical employment data to decide whom to hire, they would have very few examples of successful female scientists and engineers, for example.
Researchers Danielle Li, Lindsey R. Raymond and Peter Bergman built three resume screening algorithms for first-round interviews for high-paying jobs in industries often criticized for a lack of diversity. The approach yields more than three times as many Black and Hispanic candidates as companies may have considered using traditional resume screening algorithms.
Hiring as exploration
The third algorithm implemented a model that included ‘exploration bonuses’, which identifies candidates whose quality the firm knows the least about according to the firm’s existing data. It accounts for details such as having an unusual college major, different geographies, or unique work histories. This is referred to as ‘Hiring as Exploration‘, as the paper is called because you will never know if you don’t try. Using this third algorithm, researchers more than doubled the number of candidates who were Black or Hispanic. The first two algorithms, which used a so-called ‘standardized learning approach’, decreased the number of Black and Hispanic candidates.
The results highlight the importance of incorporating exploration in developing decision-making algorithms that are potentially both more efficient and equitable. When you incorporate exploration into the algorithm, you improve the quality of talent and hire more diverse candidates. Danielle Li, Lindsey R. Raymond and Peter Bergman warn that firms that continue to use static approaches in their algorithms risk missing out on quality applicants from different backgrounds.
You can find the paper here.
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