Study of the influence of a biased database on the prediction of standard algorithms for selecting the best candidate for an interview
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arXiv
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| Autores principales: | , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866912361420423168 |
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| author | Wang, Shuyu Saillet, Angélique Gall, Philomène Le Lacroux, Alain Martin-Lacroux, Christelle Brault, Vincent |
| author_facet | Wang, Shuyu Saillet, Angélique Gall, Philomène Le Lacroux, Alain Martin-Lacroux, Christelle Brault, Vincent |
| contents | Artificial intelligence is used at various stages of the recruitment process to automatically select the best candidate for a position, with companies guaranteeing unbiased recruitment. However, the algorithms used are either trained by humans or are based on learning from past experiences that were biased. In this article, we propose to generate data mimicking external (discrimination) and internal biases (self-censorship) in order to train five classic algorithms and to study the extent to which they do or do not find the best candidates according to objective criteria. In addition, we study the influence of the anonymisation of files on the quality of predictions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_02609 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Study of the influence of a biased database on the prediction of standard algorithms for selecting the best candidate for an interview Wang, Shuyu Saillet, Angélique Gall, Philomène Le Lacroux, Alain Martin-Lacroux, Christelle Brault, Vincent Artificial Intelligence Computers and Society Applications Methodology Artificial intelligence is used at various stages of the recruitment process to automatically select the best candidate for a position, with companies guaranteeing unbiased recruitment. However, the algorithms used are either trained by humans or are based on learning from past experiences that were biased. In this article, we propose to generate data mimicking external (discrimination) and internal biases (self-censorship) in order to train five classic algorithms and to study the extent to which they do or do not find the best candidates according to objective criteria. In addition, we study the influence of the anonymisation of files on the quality of predictions. |
| title | Study of the influence of a biased database on the prediction of standard algorithms for selecting the best candidate for an interview |
| topic | Artificial Intelligence Computers and Society Applications Methodology |
| url | https://arxiv.org/abs/2505.02609 |