Gender Representation and Bias in Indian Civil Service Mock Interviews
Fuente:
arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866929507618783232 |
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| author | Banerjee, Somonnoy Dutta, Sujan Datta, Soumyajit KhudaBukhsh, Ashiqur R. |
| author_facet | Banerjee, Somonnoy Dutta, Sujan Datta, Soumyajit KhudaBukhsh, Ashiqur R. |
| contents | This paper makes three key contributions. First, via a substantial corpus of 51,278 interview questions sourced from 888 YouTube videos of mock interviews of Indian civil service candidates, we demonstrate stark gender bias in the broad nature of questions asked to male and female candidates. Second, our experiments with large language models show a strong presence of gender bias in explanations provided by the LLMs on the gender inference task. Finally, we present a novel dataset of 51,278 interview questions that can inform future social science studies. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_12194 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Gender Representation and Bias in Indian Civil Service Mock Interviews Banerjee, Somonnoy Dutta, Sujan Datta, Soumyajit KhudaBukhsh, Ashiqur R. Computation and Language Computers and Society This paper makes three key contributions. First, via a substantial corpus of 51,278 interview questions sourced from 888 YouTube videos of mock interviews of Indian civil service candidates, we demonstrate stark gender bias in the broad nature of questions asked to male and female candidates. Second, our experiments with large language models show a strong presence of gender bias in explanations provided by the LLMs on the gender inference task. Finally, we present a novel dataset of 51,278 interview questions that can inform future social science studies. |
| title | Gender Representation and Bias in Indian Civil Service Mock Interviews |
| topic | Computation and Language Computers and Society |
| url | https://arxiv.org/abs/2409.12194 |