Professional Presentation and Projected Power: A Case Study of Implicit Gender Information in English CVs

Jinrui Yang, Sheilla Njoto, Marc Cheong, Leah Ruppanner, Lea Frermann


Abstract
Gender discrimination in hiring is a pertinent and persistent bias in society, and a common motivating example for exploring bias in NLP. However, the manifestation of gendered language in application materials has received limited attention. This paper investigates the framing of skills and background in CVs of self-identified men and women. We introduce a data set of 1.8K authentic, English-language, CVs from the US, covering 16 occupations, allowing us to partially control for the confound occupation-specific gender base rates. We find that (1) women use more verbs evoking impressions of low power; and (2) classifiers capture gender signal even after data balancing and removal of pronouns and named entities, and this holds for both transformer-based and linear classifiers.
Anthology ID:
2022.nlpcss-1.15
Volume:
Proceedings of the Fifth Workshop on Natural Language Processing and Computational Social Science (NLP+CSS)
Month:
November
Year:
2022
Address:
Abu Dhabi, UAE
Venue:
NLP+CSS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
140–150
Language:
URL:
https://aclanthology.org/2022.nlpcss-1.15
DOI:
10.18653/v1/2022.nlpcss-1.15
Bibkey:
Cite (ACL):
Jinrui Yang, Sheilla Njoto, Marc Cheong, Leah Ruppanner, and Lea Frermann. 2022. Professional Presentation and Projected Power: A Case Study of Implicit Gender Information in English CVs. In Proceedings of the Fifth Workshop on Natural Language Processing and Computational Social Science (NLP+CSS), pages 140–150, Abu Dhabi, UAE. Association for Computational Linguistics.
Cite (Informal):
Professional Presentation and Projected Power: A Case Study of Implicit Gender Information in English CVs (Yang et al., NLP+CSS 2022)
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PDF:
https://aclanthology.org/2022.nlpcss-1.15.pdf