# She Elicits Requirements and He Tests: Software Engineering Gender Bias in Large Language Models

arXiv: https://arxiv.org/abs/2303.10131 · HTML: https://mlsanity.com/paper/2303.10131/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 30 references that are in the map.
- First posted on arXiv 17 Mar 2023.

## Builds on (most cited first, 11 of 11)

- [A Survey on Bias and Fairness in Machine Learning](https://mlsanity.com/paper/1908.09635/) (2019)
- [Semantics derived automatically from language corpora contain human-like biases](https://mlsanity.com/paper/1608.07187/) (2016)
- [Towards Understanding and Mitigating Social Biases in Language Models](https://mlsanity.com/paper/2106.13219/) (2021)
- [Identifying and Reducing Gender Bias in Word-Level Language Models](https://mlsanity.com/paper/1904.03035/) (2019)
- [Reducing Sentiment Bias in Language Models via Counterfactual Evaluation](https://mlsanity.com/paper/1911.03064/) (2019)
- [Style Transfer Through Back-Translation](https://mlsanity.com/paper/1804.09000/) (2018)
- [Mitigating Political Bias in Language Models Through Reinforced Calibration](https://mlsanity.com/paper/2104.14795/) (2021)
- [Including Everyone, Everywhere: Understanding Opportunities and Challenges of Geographic Gender-Inclusion in OSS](https://mlsanity.com/paper/2010.00822/) (2020)
- [On the Unhappiness of Software Developers](https://mlsanity.com/paper/1703.04993/) (2017)
- [Worldwide Gender Differences in Public Code Contributions](https://mlsanity.com/paper/2202.07278/) (2022)
- [Exploring software developers' work practices: Task differences, participation, engagement, and speed of task resolution](https://mlsanity.com/paper/2104.07847/) (2021)

## Built on by (most cited first, 12 of 12)

- [Unveiling Gender Bias in Terms of Profession Across LLMs: Analyzing and Addressing Sociological Implications](https://mlsanity.com/paper/2307.09162/) (2023)
- [The Social Impact of Generative AI: An Analysis on ChatGPT](https://mlsanity.com/paper/2403.04667/) (2024)
- [A Comprehensive Analysis of Large Language Model Outputs: Similarity, Diversity, and Bias](https://mlsanity.com/paper/2505.09056/) (2025)
- [Generative AI and Empirical Software Engineering: A Paradigm Shift](https://mlsanity.com/paper/2502.08108/) (2025)
- [Investigating Markers and Drivers of Gender Bias in Machine Translations](https://mlsanity.com/paper/2403.11896/) (2024)
- [A Catalog of Fairness-Aware Practices in Machine Learning Engineering](https://mlsanity.com/paper/2408.16683/) (2024)
- [Large Language Model Soft Ideologization via AI-Self-Consciousness](https://mlsanity.com/paper/2309.16167/) (2023)
- [How Do Generative Models Draw a Software Engineer? A Case Study on Stable Diffusion Bias](https://mlsanity.com/paper/2501.09014/) (2025)
- [Prompt and Prejudice](https://mlsanity.com/paper/2408.04671/) (2024)
- [Evaluation of Large Language Models: STEM education and Gender Stereotypes](https://mlsanity.com/paper/2406.10133/) (2024)
- [Gender Bias in Generative AI-assisted Recruitment Processes](https://mlsanity.com/paper/2603.11736/) (2026)
- [When Who You Are Can Change the Code You Get: A Study of Persona-Induced Bias in LLM Code Generation](https://mlsanity.com/paper/2609.22102/) (2026)

## Abstract (arXiv)

Implicit gender bias in software development is a well-documented issue, such as the association of technical roles with men. To address this bias, it is important to understand it in more detail. This study uses data mining techniques to investigate the extent to which 56 tasks related to software development, such as assigning GitHub issues and testing, are affected by implicit gender bias embedded in large language models. We systematically translated each task from English into a genderless language and back, and investigated the pronouns associated with each task. Based on translating each task 100 times in different permutations, we identify a significant disparity in the gendered pronoun associations with different tasks. Specifically, requirements elicitation was associated with the pronoun "he" in only 6% of cases, while testing was associated with "he" in 100% of cases. Additionally, tasks related to helping others had a 91% association with "he" while the same association for tasks related to asking coworkers was only 52%. These findings reveal a clear pattern of gender bias related to software development tasks and have important implications for addressing this issue both in the training of large language models and in broader society.