The Impact of Artificial Intelligence and Debiasing on Applicant Quality and Gender Diversity
The use of Artificial Intelligence (AI) in hiring is becoming increasingly popular, but little is known about the direct impact of its use on applicant decisions. In a series of novel economic experiments using a representative US sample (N=1,002), we provide some of the first comprehensive causal evidence on how the use of AI and debiasing affect the quality and gender diversity of applicants. We study application decisions for competitive jobs in two experiments where participants are faced with different evaluators: human, AI, debiased human, and debiased AI. Overall, we find that the use of AI does not affect the quality and gender diversity of applicants compared to human evaluators, whereas debiasing (whether human or AI) increases gender diversity without reducing the number of high quality applicants. Our findings suggest that firms with diversity goals wishing to use AI in hiring could do so without hindering such goals, as long as their algorithm is debiased
Year of publication: |
2023
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Authors: | Awad, Edmond ; Balafoutas, Loukas ; Chen, Li ; Ip, Edwin ; Vecci, Joe |
Publisher: |
[S.l.] : SSRN |
Saved in:
freely available
Extent: | 1 Online-Ressource (30 p) |
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Type of publication: | Book / Working Paper |
Language: | English |
Notes: | Nach Informationen von SSRN wurde die ursprüngliche Fassung des Dokuments December 2022 erstellt |
Other identifiers: | 10.2139/ssrn.4351820 [DOI] |
Classification: | J23 - Employment Determination; Job Creation; Demand for Labor; Self-Employment ; J71 - Discrimination ; J78 - Public Policy |
Source: | ECONIS - Online Catalogue of the ZBW |
Persistent link: https://www.econbiz.de/10014261579