Hiring as Exploration

61 Pages Posted: 31 Aug 2020 Last revised: 15 Apr 2021

See all articles by Danielle Li

Danielle Li

Massachusetts Institute of Technology (MIT) - Sloan School of Management

Lindsey Raymond

Massachusetts Institute of Technology (MIT)

Peter Bergman

Columbia University

Date Written: August 2020

Abstract

This paper views hiring as a contextual bandit problem: to find the best workers over time, firms must balance “exploitation” (selecting from groups with proven track records) with “exploration” (selecting from under-represented groups to learn about quality). Yet modern hiring algorithms, based on “supervised learning” 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.

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Suggested Citation

Li, Danielle and Raymond, Lindsey and Bergman, Peter, Hiring as Exploration (August 2020). NBER Working Paper No. w27736, Available at SSRN: https://ssrn.com/abstract=3683612

Danielle Li (Contact Author)

Massachusetts Institute of Technology (MIT) - Sloan School of Management ( email )

100 Main Street
E62-416
Cambridge, MA 02142
United States

Lindsey Raymond

Massachusetts Institute of Technology (MIT) ( email )

77 Massachusetts Avenue
50 Memorial Drive
Cambridge, MA 02139-4307
United States

Peter Bergman

Columbia University ( email )

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