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

Mallory Avery (), Andreas Leibbrandt and Joseph Vecci ()
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Mallory Avery: Department of Economics, Monash Business School, Monash University
Joseph Vecci: Gothenburg University, Vasagatan, Gothenburg, Sweden

No 2023-09, Monash Economics Working Papers from Monash University, Department of Economics

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 in recruitment impacts 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 this is driven by female jobseekers believing that there is less bias in recruitment 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 (search for similar items in EconPapers)
Date: 2023-05
New Economics Papers: this item is included in nep-big, nep-exp, nep-gen, nep-hrm, nep-lma and nep-ltv
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