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Hiring as Exploration

Author

Listed:
  • Danielle Li
  • Lindsey R. Raymond
  • Peter Bergman

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.

Suggested Citation

  • Danielle Li & Lindsey R. Raymond & Peter Bergman, 2020. "Hiring as Exploration," NBER Working Papers 27736, National Bureau of Economic Research, Inc.
  • Handle: RePEc:nbr:nberwo:27736
    Note: CF ED LE LS POL PR TWP
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    References listed on IDEAS

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    Cited by:

    1. Peter Leopold S. Bergman & Elizabeth Kopko & Julio Rodriguez, 2021. "Using Predictive Analytics to Track Students: Evidence from a Seven-College Experiment," CESifo Working Paper Series 9157, CESifo.
    2. Mario Alberto García-Meza, 2021. "The Cost of Work Discrimination: A Market Capture Differential Game Model," Mathematics, MDPI, vol. 9(19), pages 1-10, September.
    3. Jentjens, Sabine & Cherbib, Jihène, 2023. "Trust me if you can – Do trust propensities in granting working-from-home arrangements change during times of exogenous shocks?," Journal of Business Research, Elsevier, vol. 161(C).
    4. Zhang, Lixuan & Yencha, Christopher, 2022. "Examining perceptions towards hiring algorithms," Technology in Society, Elsevier, vol. 68(C).
    5. Richard T. Carson & Joshua Graff Zivin & Jordan J. Louviere & Sally Sadoff & Jeffrey G. Shrader, 2022. "The Risk of Caution: Evidence from an Experiment," Management Science, INFORMS, vol. 68(12), pages 9042-9060, December.
    6. Diana Moreira & Santiago Pérez, 2022. "Who Benefits from Meritocracy?," NBER Working Papers 30113, National Bureau of Economic Research, Inc.
    7. Jonas Radbruch & Amelie Schiprowski, 2020. "Interview Sequences and the Formation of Subjective Assessments," ECONtribute Discussion Papers Series 045, University of Bonn and University of Cologne, Germany.
    8. Laura Blattner & Scott Nelson & Jann Spiess, 2021. "Unpacking the Black Box: Regulating Algorithmic Decisions," Papers 2110.03443, arXiv.org, revised May 2024.
    9. Laschever, Ron A. & Weinstein, Russell, 2021. "Preference Signaling and Worker-Firm Matching: Evidence from Interview Auctions," IZA Discussion Papers 14622, Institute of Labor Economics (IZA).
    10. Claudio Cardoso Flores & Marcelo Cunha Medeiros, 2020. "Online Action Learning in High Dimensions: A Conservative Perspective," Papers 2009.13961, arXiv.org, revised Mar 2024.
    11. Jin Li & Ye Luo & Xiaowei Zhang, 2021. "Causal Reinforcement Learning: An Instrumental Variable Approach," Papers 2103.04021, arXiv.org, revised Sep 2022.
    12. Fumagalli, Elena & Rezaei, Sarah & Salomons, Anna, 2022. "OK computer: Worker perceptions of algorithmic recruitment," Research Policy, Elsevier, vol. 51(2).
    13. Luca Coraggio & Marco Pagano & Annalisa Scognamiglio & Joacim Tåg, 2022. "JAQ of All Trades: Job Mismatch, Firm Productivity and Managerial Quality," EIEF Working Papers Series 2205, Einaudi Institute for Economics and Finance (EIEF), revised Mar 2022.
    14. Marina Chugunova & Wolfgang J. Luhan, 2022. "Ruled by robots: Preference for algorithmic decision makers and perceptions of their choices," Working Papers in Economics & Finance 2022-03, University of Portsmouth, Portsmouth Business School, Economics and Finance Subject Group.
    15. Lepage, Louis Pierre, 2021. "Endogenous learning, persistent employer biases, and discrimination," CLEF Working Paper Series 34, Canadian Labour Economics Forum (CLEF), University of Waterloo.
    16. Moran Koren, 2023. "The Gatekeeper Effect: The Implications of Pre-Screening, Self-selection, and Bias for Hiring Processes," Papers 2312.17167, arXiv.org.
    17. Pisanelli, Elena, 2022. "Your resume is your gatekeeper: Automated resume screening as a strategy to reduce gender gaps in hiring," Economics Letters, Elsevier, vol. 221(C).
    18. Victor Alfonso Naya & Guillaume Bied & Philippe Caillou & Bruno Crépon & Christophe Gaillac & Elia Pérennes & Michèle Sebag, 2021. "Designing labor market recommender systems: the importance of job seeker preferences and competition," Post-Print hal-03540319, HAL.

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    More about this item

    JEL classification:

    • D80 - Microeconomics - - Information, Knowledge, and Uncertainty - - - General
    • J20 - Labor and Demographic Economics - - Demand and Supply of Labor - - - General
    • M15 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Business Administration - - - IT Management
    • M51 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Personnel Economics - - - Firm Employment Decisions; Promotions
    • O33 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Technological Change: Choices and Consequences; Diffusion Processes

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