Hiring as Exploration.
This article 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 o...
| Publicado en: | Review of Economic Studies Vol. 93; no. 2; pp. 1200 - 1241 |
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| Autores principales: | , , |
| Formato: | Artículo |
| Publicado: |
Oxford University Press / USA
Mar2026
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| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=192334031&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 192334031 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 00346527 REM jtl: Review of Economic Studies issn: 00346527 maglogo: N pubinfo: dt: Mar2026 vid: 93 iid: 2 pid: 622 pub: Oxford University Press / USA artinfo: ui: 192334031 10.1093/restud/rdaf040 ppf: 1200 ppct: 41 formats: tig: atl: Hiring as Exploration. aug: au: Li, Danielle Raymond, Lindsey Bergman, Peter affil: Massachusetts Institute of Technology and National Bureau of Economic Research, USA Massachusetts Institute of Technology, USA The University of Texas at Austin and National Bureau of Economic Research, USA su: Decision making Employee recruitment Algorithms Job resumes Supervised learning sug: subj: Decision making Administration of Human Resource Programs (except Education, Public Health, and Veterans' Affairs Programs) Human Resources Consulting Services Employee recruitment Algorithms Job resumes Supervised learning keyword: Algorithmic fairness Contextual bandits copyrightHolder:Review of Economic Studies Ltd copyrightYear:2026 Hiring inLanguage:en Job search Machine learning publisher:Oxford University Press sameAs:https://dx.doi.org/10.1093/restud/rdaf040 Algorithmic fairness Contextual bandits copyrightHolder:Review of Economic Studies Ltd copyrightYear:2026 Hiring inLanguage:en Job search Machine learning publisher:Oxford University Press sameAs:https://dx.doi.org/10.1093/restud/rdaf040 ab: This article 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. Together, our results highlight the importance of incorporating exploration in developing decision-making algorithms that are potentially both more efficient and equitable. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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