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PreprintCited by 44Ethics and Social Impacts of AINames, Identity, and Discrimination ResearchAI and HR Technologies

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

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Avery, Mallory, Leibbrandt, Andreas, Vecci, Joseph·2023·SSRN Electronic Journal
DOI 10.2139/ssrn.4370805
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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 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.

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  • The simple fact of using algorithmic decision-making can reduce avoidance by vulnerable candidates.

    “The simple fact of using algorithmic decision-making can reduce avoidance by vulnerable candidates [13].”

    From Fairness and Bias in Algorithmic Hiring: A Multidisciplinary Survey (2025) · cites this paper as [13]

    In “The Application Decision” (page unavailable), the paper reports that announcing AI rather than human evaluation increased women’s completion of a tech job application. Section 4.4 links this to lower anticipated bias. This supports reduced application avoidance for women in the studied setting, but not the broader claim about vulnerable candidates generally or algorithmic decision-making in other settings. (LLM, conf=0.82)

    Experimental — accuracy not yet measured

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Cited by

Sorted by citations

Authors
Avery, M., Leibbrandt, A., & Vecci, J.
Affiliations
Monash University (AU)
Corresponding author
Avery, Mallory
ORCID
Leibbrandt, Andreas
Venue
SSRN Electronic Journal
Publisher
Elsevier BV
Published
2023
ISSN
1556-5068 (ISSN-L)
DOI
10.2139/ssrn.4370805
OpenAlex
W4322742241
Cited by
44
References
0
Citations by year
2026: 3 · 2025: 28 · 2024: 11 · 2023: 2
Type
posted content
Language
en
Open access
Locations
  • SSRN Electronic Journal (acceptedVersion)
  • RePEc: Research Papers in Economics (submittedVersion)
  • Econstor (Econstor) (submittedVersion)
Topics
  • Social Sciences › Safety Research › Ethics and Social Impacts of AI
  • Social Sciences › Sociology and Political Science › Names, Identity, and Discrimination Research
  • Social Sciences › Business, Management and Accounting › Organizational Behavior and Human Resource Management › AI and HR Technologies
Keywords
artificial intelligence in recruitment, gender diversity, gender gap, technology industry, labor supply, labor demand, applicant screening, hiring discrimination

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