Algorithmic Bias and the Power of Code: Investigating the Reproduction of Gender Structures in the Development of Computer Technologies

Heying Bai

doi:10.54254/2755-2721/2025.25254

Applied and Computational EngineeringOpen access

Algorithmic Bias and the Power of Code: Investigating the Reproduction of Gender Structures in the Development of Computer Technologies

Research Article

Open Access

Algorithmic Bias and the Power of Code: Investigating the Reproduction of Gender Structures in the Development of Computer Technologies

Heying Bai ^1*

¹ Sun Yat-sen University, Guangzhou, China

^*Corresponding author: rara481846778@gmail.com

Published on 20 July 2025

ACE Vol.173

ISSN (Print): 2755-273X

ISSN (Online): 2755-2721

ISBN (Print): 978-1-80590-231-7

ISBN (Online): 978-1-80590-232-4

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Abstract

This study explores how everyday programming practices reproduce and amplify gender bias in software systems. By analyzing five widely used open-source repositories in the fields of recruitment and financial analysis (totaling over 200,000 lines of code), and conducting in-depth interviews with ten developers, we identified three common patterns of bias: rigidly coded binary gender labels, stereotype-driven feature engineering (such as “vacancy years”), and the negation of non-binary genders. Static code analysis marked these trends, while dynamic testing on the gender-balanced dataset showed that the false negative rate for female users could be up to 15 percentage points higher, and there was a 10% inflation in the risk score. The interview revealed that organizational pressures (tight deadlines, lack of built-in fairness tools, and lack of procedural guidelines) led designers to be unable to mitigate bias. Based on the above findings, we propose a method for classifying “bias-prone code features,” an integration strategy for automatically detecting bias in continuous integration, and targeted code review guidelines. This achievement not only provides empirical evidence of code-level unfairness but also presents practical suggestions for integrating fairness into the software development lifecycle, thereby promoting a more equitable social technology system.

Keywords:

Algorithmic bias, gender structures, code review, socio‑technical systems, equitable computing

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References

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References

[7]. González, J. A., & Smith, K. (2023). Dealing with gender bias issues in dataalgorithmic processes: A socialstatistical perspective. Algorithms, 15(9), 303. https: //doi.org/10.3390/a15090303

[8]. Sultana, S., Turzo, A. K., & Bosu, A. (2022). Code reviews in open source projects: How do gender biases affect participation and outcomes? arXiv Preprint arXiv: 2210.00139.

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[11]. Park, J., & Lee, H. (2025). FairCode: Evaluating social bias of large language models in code generation. arXiv Preprint arXiv: 2501.05396.

Cite this article

Bai,H. (2025). Algorithmic Bias and the Power of Code: Investigating the Reproduction of Gender Structures in the Development of Computer Technologies. Applied and Computational Engineering,173,50-56.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

About volume

Volume title: Proceedings of the 7th International Conference on Computing and Data Science

ISBN: 978-1-80590-231-7(Print) / 978-1-80590-232-4(Online)

Editor: Marwan Omar

Conference website: https://2025.confcds.org/

Conference date: 25 September 2025

Series: Applied and Computational Engineering

Volume number: Vol.173

ISSN: 2755-2721(Print) / 2755-273X(Online)

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