Hiring as Exploration

60 Pages Posted: 13 Jul 2020 Last revised: 20 Aug 2020

See all articles by Danielle Li

Danielle Li

Massachusetts Institute of Technology (MIT)

Lindsey Raymond

Massachusetts Institute of Technology (MIT)

Peter Bergman

Columbia University

Date Written: June 18, 2020

Abstract

In looking for 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. In this paper, we view hiring as a contextual bandit problem and 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 quality over time. Together, our results highlight the importance of incorporating exploration in developing decision-making algorithms that are potentially both more efficient and equitable.

Keywords: Hiring, Machine Learning, Contextual Bandits, Labor Market Discrimination, Algorithmic Bias, Algorithmic Learning, Reinforcement Learning

JEL Classification: J24, J70, M15, M51

Suggested Citation

Li, Danielle and Raymond, Lindsey and Bergman, Peter, Hiring as Exploration (June 18, 2020). Available at SSRN: https://ssrn.com/abstract=3630630 or http://dx.doi.org/10.2139/ssrn.3630630

Danielle Li (Contact Author)

Massachusetts Institute of Technology (MIT) ( email )

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