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Cited by 49Ethics and Social Impacts of AIGender Studies in LanguageNatural Language Processing Techniques

Theories of ”gender” in NLP bias research

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Devinney, Hannah, Björklund, Jenny, Björklund, Henrik·2022·2022 ACM Conference on Fairness Accountability and Transparency
DOI 10.1145/3531146.3534627
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Abstract

The rise of concern around Natural Language Processing (NLP) technologies containing and perpetuating social biases has led to a rich and rapidly growing area of research. Gender bias is one of the central biases being analyzed, but to date there is no comprehensive analysis of how “gender” is theorized in the field. We survey nearly 200 articles concerning gender bias in NLP to discover how the field conceptualizes gender both explicitly (e.g. through definitions of terms) and implicitly (e.g. through how gender is operationalized in practice). In order to get a better idea of emerging trajectories of thought, we split these articles into two sections by time.

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  • Modeling gender as binary and immutable erases the identities of trans, nonbinary, and intersex people.

    “Devinney, Björklund, and Björklund (2022) argue that modeling gender as binary and immutable erases the identities of trans, nonbinary, and intersex people.”

    From Bias and Fairness in Large Language Models: A Survey (2024) · cites this paper as Devinney, Björklund, and Björklund (2022)

    Section 2.2 (page unavailable) describes the binary, immutable “folk” model and explicitly says it erases trans and nonbinary people. It also notes that intersex people show sex is not binary, but the body-text passages do not explicitly say this model erases intersex identities. (LLM, conf=0.84)

    Experimental — accuracy not yet measured

  • Word lists are often limited in length and scope.

    “Furthermore, word lists are often limited in length and scope, may depend on proxies ( e.g. , names as a proxy for gender) that are often tied to other social identities, and utilize word pairs that are not semantically or connotatively equivalent (Devinney, Björklund, and Björklund 2022).”

    From Bias and Fairness in Large Language Models: A Survey (2024) · cites this paper as Devinney, Björklund, and Björklund 2022

    Section 4.2 (page unavailable) says word pairs and lists occasionally use a limited selection of first names and warns that such names are tied to particular generations, races, and religions. This supports a limitation in some lists, but the copy does not establish that word lists are often limited in overall length and scope. (LLM, conf=0.74)

    Experimental — accuracy not yet measured

  • Word lists may depend on proxies that are often tied to other social identities.

    “Furthermore, word lists are often limited in length and scope, may depend on proxies ( e.g. , names as a proxy for gender) that are often tied to other social identities, and utilize word pairs that are not semantically or connotatively equivalent (Devinney, Björklund, and Björklund 2022).”

    From Bias and Fairness in Large Language Models: A Survey (2024) · cites this paper as Devinney, Björklund, and Björklund 2022

  • Word lists utilize word pairs that are not semantically or connotatively equivalent.

    “Furthermore, word lists are often limited in length and scope, may depend on proxies ( e.g. , names as a proxy for gender) that are often tied to other social identities, and utilize word pairs that are not semantically or connotatively equivalent (Devinney, Björklund, and Björklund 2022).”

    From Bias and Fairness in Large Language Models: A Survey (2024) · cites this paper as Devinney, Björklund, and Björklund 2022

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200 of 211 shown

Cited by

Sorted by citations

Authors
Devinney, H., Björklund, J., & Björklund, H.
Affiliations
  • Umeå University (SE)
  • Uppsala University (SE)
ORCID
Venue
2022 ACM Conference on Fairness Accountability and Transparency
Publisher
ACM
Published
2022-06-20
Pages
2083-2102
DOI
10.1145/3531146.3534627
OpenAlex
W4283162604
Cited by
49
References
5
Citations by year
2026: 6 · 2025: 12 · 2024: 13 · 2023: 15 · 2022: 3
FWCI
9.83
Type
conference paper
Language
en
Open access
Licence
Locations
  • ACM Conference on Fairness, Accountability, and Transparency (FAccT) (publishedVersion, cc-by-nc)
  • arXiv (Cornell University) (submittedVersion)
  • DiVA at Umeå University (Umeå University) (submittedVersion, other-oa)
  • Uppsala University Publications (Uppsala University) (submittedVersion, other-oa)
  • arXiv (Cornell University)
Topics
  • Social Sciences › Safety Research › Ethics and Social Impacts of AI
  • Social Sciences › Gender Studies › Gender Studies in Language
  • Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
Keywords
gender bias, natural language processing, terminology, fairness in NLP, conceptual history

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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

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