This paper views hiring as a contextual bandit problem: to find the best workers over time, firms must balance \u201cexploitation\u201d (selecting from groups with proven track records) with \u201cexploration\u201d (selecting from under-represented groups to learn about quality). Yet modern hiring algorithms, based on \u201csupervised learning\u201d approaches, are designed solely for exploitation. Instead, we build a resume screening algorithm that values exploration by evaluating candidates according to their statistical upside potential. Using data from professional services recruiting within a Fortune 500 firm, we show that this approach improves the quality (as measured by eventual hiring rates) of candidates selected for an interview, while also increasing demographic diversity, relative to the firm’s existing practices. The same is not true for traditional supervised learning based algorithms, which improve hiring rates but select far fewer Black and Hispanic applicants. In an extension, we show that exploration-based algorithms are also able to learn more effectively about simulated changes in applicant hiring potential over time. Together, our results highlight the importance of incorporating exploration in developing decision-making algorithms that are potentially both more efficient and equitable.<\/p>\n","protected":false},"excerpt":{"rendered":"
This paper views hiring as a contextual bandit problem: to find the best workers over time, firms must balance \u201cexploitation\u201d (selecting from groups with proven track records) with \u201cexploration\u201d (selecting from under-represented groups to learn about quality). Yet modern hiring algorithms, based on \u201csupervised learning\u201d approaches, are designed solely for exploitation. Instead, we build a 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