Does Artificial Intelligence Help or Hurt Gender Diversity? Evidence from Two Field Experiments on Recruitment in Tech

Mallory Avery, Andreas Leibbrandt and Joseph Vecci

No 10996, CESifo Working Paper Series from CESifo

Abstract: The use of Artificial Intelligence (AI) in recruitment is rapidly increasing and drastically changing how people apply to jobs and how applications are reviewed. In this paper, we use two field experiments to study how AI recruitment tools can impact gender diversity in the male-dominated technology sector, both overall and separately for labor supply and demand. We find that the use of AI in recruitment changes the gender distribution of potential hires, in some cases more than doubling the fraction of top applicants that are women. This change is generated by better outcomes for women in both supply and demand. On the supply side, we observe that the use of AI reduces the gender gap in application completion rates. Complementary survey evidence suggests that anticipated bias is a driver of increased female application completion when assessed by AI instead of human evaluators. On the demand side, we find that providing evaluators with applicants’ AI scores closes the gender gap in assessments that otherwise disadvantage female applicants. Finally, we show that the AI tool would have to be substantially biased against women to result in a lower level of gender diversity than found without AI.

Keywords: artificial intelligence; gender; diversity; field experiment (search for similar items in EconPapers)
JEL-codes: C93 J23 J71 J78 (search for similar items in EconPapers)
Date: 2024
New Economics Papers: this item is included in nep-ain, nep-exp, nep-hrm and nep-lma
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Related works:
Working Paper: Does Artificial Intelligence Help or Hurt Gender Diversity? Evidence from Two Field Experiments on Recruitment in Tech (2023)
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Persistent link: https://EconPapers.repec.org/RePEc:ces:ceswps:_10996

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