Algorithmic Bias and the Power of Code: Investigating the Reproduction of Gender Structures in the Development of Computer Technologies
Year: 2025
Type: article
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.
Author: Heying Bai
Institution: Sun Yat-sen University
Language: English
Subfield: Safety Research
Field: Social Sciences
Domain: Social Sciences
Open Access status: hybrid
OpenAlex ID: W4412926987