@inproceedings{yang-etal-2022-professional,
title = "Professional Presentation and Projected Power: A Case Study of Implicit Gender Information in {E}nglish {CV}s",
author = "Yang, Jinrui and
Njoto, Sheilla and
Cheong, Marc and
Ruppanner, Leah and
Frermann, Lea",
editor = "Bamman, David and
Hovy, Dirk and
Jurgens, David and
Keith, Katherine and
O'Connor, Brendan and
Volkova, Svitlana",
booktitle = "Proceedings of the Fifth Workshop on Natural Language Processing and Computational Social Science (NLP+CSS)",
month = nov,
year = "2022",
address = "Abu Dhabi, UAE",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.nlpcss-1.15",
doi = "10.18653/v1/2022.nlpcss-1.15",
pages = "140--150",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Professional Presentation and Projected Power: A Case Study of Implicit Gender Information in English CVs
%A Yang, Jinrui
%A Njoto, Sheilla
%A Cheong, Marc
%A Ruppanner, Leah
%A Frermann, Lea
%Y Bamman, David
%Y Hovy, Dirk
%Y Jurgens, David
%Y Keith, Katherine
%Y O’Connor, Brendan
%Y Volkova, Svitlana
%S Proceedings of the Fifth Workshop on Natural Language Processing and Computational Social Science (NLP+CSS)
%D 2022
%8 November
%I Association for Computational Linguistics
%C Abu Dhabi, UAE
%F yang-etal-2022-professional
%X 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.
%R 10.18653/v1/2022.nlpcss-1.15
%U https://aclanthology.org/2022.nlpcss-1.15
%U https://doi.org/10.18653/v1/2022.nlpcss-1.15
%P 140-150
Markdown (Informal)
[Professional Presentation and Projected Power: A Case Study of Implicit Gender Information in English CVs](https://aclanthology.org/2022.nlpcss-1.15) (Yang et al., NLP+CSS 2022)
ACL