# Transcending the "Male Code": Implicit Masculine Biases in NLP Contexts

arXiv: https://arxiv.org/abs/2304.12810 · HTML: https://mlsanity.com/paper/2304.12810/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 114 references that are in the map.
- First posted on arXiv 22 Apr 2023.

## Builds on (most cited first, 20 of 20)

- [Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings](https://mlsanity.com/paper/1607.06520/) (2016)
- [Model Cards for Model Reporting](https://mlsanity.com/paper/1810.03993/) (2018)
- [Language (Technology) is Power: A Critical Survey of "Bias" in NLP](https://mlsanity.com/paper/2005.14050/) (2020)
- [The State and Fate of Linguistic Diversity and Inclusion in the NLP World](https://mlsanity.com/paper/2004.09095/) (2020)
- [Mitigating Gender Bias in Natural Language Processing: Literature Review](https://mlsanity.com/paper/1906.08976/) (2019)
- [Challenges and Strategies in Cross-Cultural NLP](https://mlsanity.com/paper/2203.10020/) (2022)
- [SLURP: A Spoken Language Understanding Resource Package](https://mlsanity.com/paper/2011.13205/) (2020)
- [Towards Deep Conversational Recommendations](https://mlsanity.com/paper/1812.07617/) (2018)
- [Black is to Criminal as Caucasian is to Police: Detecting and Removing Multiclass Bias in Word Embeddings](https://mlsanity.com/paper/1904.04047/) (2019)
- [Harms of Gender Exclusivity and Challenges in Non-Binary Representation in Language Technologies](https://mlsanity.com/paper/2108.12084/) (2021)
- [Multi-Dimensional Gender Bias Classification](https://mlsanity.com/paper/2005.00614/) (2020)
- [Racial Disparity in Natural Language Processing: A Case Study of Social Media African-American English](https://mlsanity.com/paper/1707.00061/) (2017)
- [Just Say No: Analyzing the Stance of Neural Dialogue Generation in Offensive Contexts](https://mlsanity.com/paper/2108.11830/) (2021)
- [Welcome to the Modern World of Pronouns: Identity-Inclusive Natural Language Processing beyond Gender](https://mlsanity.com/paper/2202.11923/) (2022)
- [Unsupervised Discovery of Implicit Gender Bias](https://mlsanity.com/paper/2004.08361/) (2020)
- [The State of Speech in HCI: Trends, Themes and Challenges](https://mlsanity.com/paper/1810.06828/) (2018)
- [Eliciting and Analysing Users' Envisioned Dialogues with Perfect Voice Assistants](https://mlsanity.com/paper/2102.13508/) (2021)
- [What Pronouns for Pepper? A Critical Review of Gender/ing in Research](https://mlsanity.com/paper/2212.04206/) (2022)
- [WordBias: An Interactive Visual Tool for Discovering Intersectional Biases Encoded in Word Embeddings](https://mlsanity.com/paper/2103.03598/) (2021)
- [Mitigating Gender Bias in Machine Learning Data Sets](https://mlsanity.com/paper/2005.06898/) (2020)

## Built on by (most cited first, 5 of 5)

- [Silver-Tongued and Sundry: Exploring Intersectional Pronouns with ChatGPT](https://mlsanity.com/paper/2405.08238/) (2024)
- [Coimagining the Future of Voice Assistants with Cultural Sensitivity](https://mlsanity.com/paper/2403.17599/) (2024)
- [Inter(sectional) Alia(s): Ambiguity in Voice Agent Identity via Intersectional Japanese Self-Referents](https://mlsanity.com/paper/2506.01998/) (2025)
- [Masculine Defaults via Gendered Discourse in Podcasts and Large Language Models](https://mlsanity.com/paper/2504.11431/) (2025)
- [Operationalizing Perceptions of Agent Gender: Foundations and Guidelines](https://mlsanity.com/paper/2603.26682/) (2026)

## Abstract (arXiv)

Critical scholarship has elevated the problem of gender bias in data sets used to train virtual assistants (VAs). Most work has focused on explicit biases in language, especially against women, girls, femme-identifying people, and genderqueer folk; implicit associations through word embeddings; and limited models of gender and masculinities, especially toxic masculinities, conflation of sex and gender, and a sex/gender binary framing of the masculine as diametric to the feminine. Yet, we must also interrogate how masculinities are "coded" into language and the assumption of "male" as the linguistic default: implicit masculine biases. To this end, we examined two natural language processing (NLP) data sets. We found that when gendered language was present, so were gender biases and especially masculine biases. Moreover, these biases related in nuanced ways to the NLP context. We offer a new dictionary called AVA that covers ambiguous associations between gendered language and the language of VAs.