Pith. sign in

REVIEW 4 cited by

Harms of Gender Exclusivity and Challenges in Non-Binary Representation in Language Technologies

Not yet reviewed by Pith; the record is open.

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2108.12084 v2 pith:KVTOORK5 submitted 2021-08-27 cs.CL cs.AIcs.LG

classification cs.CLcs.AIcs.LG
keywords genderlanguageharmsnon-binarybinarychallengescurrentperpetuate
verification ladder T0 review T1 audit T2 compute T3 formal

Signed reviews

No signed human review yet.

0 comments
read the original abstract

Gender is widely discussed in the context of language tasks and when examining the stereotypes propagated by language models. However, current discussions primarily treat gender as binary, which can perpetuate harms such as the cyclical erasure of non-binary gender identities. These harms are driven by model and dataset biases, which are consequences of the non-recognition and lack of understanding of non-binary genders in society. In this paper, we explain the complexity of gender and language around it, and survey non-binary persons to understand harms associated with the treatment of gender as binary in English language technologies. We also detail how current language representations (e.g., GloVe, BERT) capture and perpetuate these harms and related challenges that need to be acknowledged and addressed for representations to equitably encode gender information.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Understanding Gender Bias in AI-Generated Product Descriptions

    cs.CL 2025-06 conditional novelty 6.0 of 10

    AI-generated product descriptions on eBay show systematic gender bias, including body-size exclusions, stereotyped feature emphasis, and differences in calls to action.

  2. The Cake that is Intelligence and Who Gets to Bake it: An AI Analogy and its Implications for Participation

    cs.AI 2025-02 accept novelty 5.0 of 10

    The authors expand LeCun's cake metaphor to the full AI lifecycle and argue that social outcomes are constrained by technical foundations such as the i.i.d. assumption, homogenization, catastrophic forgetting, and sur...

  3. Vision-Language Models Generate More Homogeneous Stories for Phenotypically Black Individuals

    cs.CV 2024-12 conditional novelty 5.0 of 10

    VLMs generate more homogeneous stories about Black individuals with higher perceived racial phenotypicality, with effects varying by gender and model.

  4. The Generative AI Ethics Playbook

    cs.CY 2024-12 conditional novelty 4.0 of 10

    A structured playbook that collects existing guidance, checklists, and case studies to help generative AI practitioners identify and mitigate ethical harms across six lifecycle stages.

Pith tools