Gender equity in hiring: examining the effectiveness of a personality-based algorithm
IntroductionGender biases in hiring decisions remain an issue in the workplace. Also, current gender balancing techniques are scientifically poorly supported and lead to undesirable results, sometimes even contributing to activating stereotypes. While hiring algorithms could bring a solution, they a...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-08-01
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| Series: | Frontiers in Psychology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fpsyg.2023.1219865/full |
| _version_ | 1797742081901330432 |
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| author | Emeric Kubiak Maria I. Efremova Maria I. Efremova Simon Baron Keely J. Frasca |
| author_facet | Emeric Kubiak Maria I. Efremova Maria I. Efremova Simon Baron Keely J. Frasca |
| author_sort | Emeric Kubiak |
| collection | DOAJ |
| description | IntroductionGender biases in hiring decisions remain an issue in the workplace. Also, current gender balancing techniques are scientifically poorly supported and lead to undesirable results, sometimes even contributing to activating stereotypes. While hiring algorithms could bring a solution, they are still often regarded as tools amplifying human prejudices. In this sense, talent specialists tend to prefer recommendations from experts, while candidates question the fairness of such tools, in particular, due to a lack of information and control over the standardized assessment. However, there is evidence that building algorithms based on data that is gender-blind, like personality - which has been shown to be mostly similar between genders, and is also predictive of performance, could help in reducing gender biases in hiring. The goal of this study was, therefore, to test the adverse impact of a personality-based algorithm across a large array of occupations.MethodThe study analyzed 208 predictive models designed for 18 employers. These models were tested on a global sample of 273,293 potential candidates for each respective role.ResultsMean weighted impact ratios of 0.91 (Female-Male) and 0.90 (Male-Female) were observed. We found similar results when analyzing impact ratios for 21 different job categories.DiscussionOur results suggest that personality-based algorithms could help organizations screen candidates in the early stages of the selection process while mitigating the risks of gender discrimination. |
| first_indexed | 2024-03-12T14:35:53Z |
| format | Article |
| id | doaj.art-0f8f795ca18b4b83b968b31d70c57d60 |
| institution | Directory Open Access Journal |
| issn | 1664-1078 |
| language | English |
| last_indexed | 2024-03-12T14:35:53Z |
| publishDate | 2023-08-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Psychology |
| spelling | doaj.art-0f8f795ca18b4b83b968b31d70c57d602023-08-17T04:34:25ZengFrontiers Media S.A.Frontiers in Psychology1664-10782023-08-011410.3389/fpsyg.2023.12198651219865Gender equity in hiring: examining the effectiveness of a personality-based algorithmEmeric Kubiak0Maria I. Efremova1Maria I. Efremova2Simon Baron3Keely J. Frasca4AssessFirst, Paris, FranceAssessFirst, Paris, FranceKing’s College London, Institute of Psychiatry, Psychology and Neuroscience, University of London, London, United KingdomAssessFirst, Paris, FranceBirkbeck Business School, Faculty of Business and Law, Birkbeck, University of London, London, United KingdomIntroductionGender biases in hiring decisions remain an issue in the workplace. Also, current gender balancing techniques are scientifically poorly supported and lead to undesirable results, sometimes even contributing to activating stereotypes. While hiring algorithms could bring a solution, they are still often regarded as tools amplifying human prejudices. In this sense, talent specialists tend to prefer recommendations from experts, while candidates question the fairness of such tools, in particular, due to a lack of information and control over the standardized assessment. However, there is evidence that building algorithms based on data that is gender-blind, like personality - which has been shown to be mostly similar between genders, and is also predictive of performance, could help in reducing gender biases in hiring. The goal of this study was, therefore, to test the adverse impact of a personality-based algorithm across a large array of occupations.MethodThe study analyzed 208 predictive models designed for 18 employers. These models were tested on a global sample of 273,293 potential candidates for each respective role.ResultsMean weighted impact ratios of 0.91 (Female-Male) and 0.90 (Male-Female) were observed. We found similar results when analyzing impact ratios for 21 different job categories.DiscussionOur results suggest that personality-based algorithms could help organizations screen candidates in the early stages of the selection process while mitigating the risks of gender discrimination.https://www.frontiersin.org/articles/10.3389/fpsyg.2023.1219865/fullgenderbiasalgorithmpersonalityhiring |
| spellingShingle | Emeric Kubiak Maria I. Efremova Maria I. Efremova Simon Baron Keely J. Frasca Gender equity in hiring: examining the effectiveness of a personality-based algorithm Frontiers in Psychology gender bias algorithm personality hiring |
| title | Gender equity in hiring: examining the effectiveness of a personality-based algorithm |
| title_full | Gender equity in hiring: examining the effectiveness of a personality-based algorithm |
| title_fullStr | Gender equity in hiring: examining the effectiveness of a personality-based algorithm |
| title_full_unstemmed | Gender equity in hiring: examining the effectiveness of a personality-based algorithm |
| title_short | Gender equity in hiring: examining the effectiveness of a personality-based algorithm |
| title_sort | gender equity in hiring examining the effectiveness of a personality based algorithm |
| topic | gender bias algorithm personality hiring |
| url | https://www.frontiersin.org/articles/10.3389/fpsyg.2023.1219865/full |
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