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An Empirical Study on the Impact of Gender Diversity on Code Quality in AI Systems

Shamse Tasnim Cynthia, Banani Roy

TL;DR

This paper investigates how gender diversity within AI development teams relates to repository popularity, code quality, and individual contributions in open-source AI projects. The authors construct a dataset of 195 GitHub AI repositories, assign contributor genders, and compare diverse versus non-diverse teams using nonparametric tests and SonarQube metrics. Key findings show diverse repositories attract greater engagement and tend to have higher-quality code; female contributors, while underrepresented, produce higher-quality code in diverse teams. The work highlights practical implications for inclusive AI development to improve reliability and reduce technical debt, and suggests avenues for cross-platform replication and broader gender modeling.

Abstract

The rapid advancement of AI systems necessitates high-quality, sustainable code to ensure reliability and mitigate risks such as bias and technical debt. However, the underrepresentation of women in software engineering raises concerns about homogeneity in AI development. Studying gender diversity in AI systems is crucial, as diverse perspectives are essential for improving system robustness, reducing bias, and enhancing overall code quality. While prior research has demonstrated the benefits of diversity in general software teams, its specific impact on the code quality of AI systems remains unexplored. This study addresses this gap by examining how gender diversity within AI teams influences project popularity, code quality, and individual contributions. Our study makes three key contributions. First, we analyzed the relationship between team diversity and repository popularity, revealing that diverse AI repositories not only differ significantly from non-diverse ones but also achieve higher popularity and greater community engagement. Second, we explored the effect of diversity on the overall code quality of AI systems and found that diverse repositories tend to have superior code quality compared to non-diverse ones. Finally, our analysis of individual contributions revealed that although female contributors contribute to a smaller proportion of the total code, their contributions demonstrate consistently higher quality than those of their male counterparts. These findings highlight the need to remove barriers to female participation in AI development, as greater diversity can improve the overall quality of AI systems.

An Empirical Study on the Impact of Gender Diversity on Code Quality in AI Systems

TL;DR

This paper investigates how gender diversity within AI development teams relates to repository popularity, code quality, and individual contributions in open-source AI projects. The authors construct a dataset of 195 GitHub AI repositories, assign contributor genders, and compare diverse versus non-diverse teams using nonparametric tests and SonarQube metrics. Key findings show diverse repositories attract greater engagement and tend to have higher-quality code; female contributors, while underrepresented, produce higher-quality code in diverse teams. The work highlights practical implications for inclusive AI development to improve reliability and reduce technical debt, and suggests avenues for cross-platform replication and broader gender modeling.

Abstract

The rapid advancement of AI systems necessitates high-quality, sustainable code to ensure reliability and mitigate risks such as bias and technical debt. However, the underrepresentation of women in software engineering raises concerns about homogeneity in AI development. Studying gender diversity in AI systems is crucial, as diverse perspectives are essential for improving system robustness, reducing bias, and enhancing overall code quality. While prior research has demonstrated the benefits of diversity in general software teams, its specific impact on the code quality of AI systems remains unexplored. This study addresses this gap by examining how gender diversity within AI teams influences project popularity, code quality, and individual contributions. Our study makes three key contributions. First, we analyzed the relationship between team diversity and repository popularity, revealing that diverse AI repositories not only differ significantly from non-diverse ones but also achieve higher popularity and greater community engagement. Second, we explored the effect of diversity on the overall code quality of AI systems and found that diverse repositories tend to have superior code quality compared to non-diverse ones. Finally, our analysis of individual contributions revealed that although female contributors contribute to a smaller proportion of the total code, their contributions demonstrate consistently higher quality than those of their male counterparts. These findings highlight the need to remove barriers to female participation in AI development, as greater diversity can improve the overall quality of AI systems.
Paper Structure (18 sections, 8 figures, 3 tables)

This paper contains 18 sections, 8 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Methodology
  4. Dataset Construction
  5. Extracting the repository features
  6. Identifying the genders of contributors
  7. Annotating team diversity
  8. Analyzing the impact of diversity in AI repositories
  9. Assessing the code quality of AI repositories
  10. Comparing code quality between male and female contributors
  11. Study Finding
  12. Impact of Gender Diversity in AI Repositories (RQ1)
  13. Code Quality in Diverse vs. Non-Diverse AI Repositories (RQ2)
  14. Quality of Code Authored by Female vs. Male Developers in AI Diverse Teams (RQ3)
  15. Key Findings
  16. ...and 3 more sections

Figures (8)

  • Figure 1: Repository-engagement and popularity features comparison of diverse and non-diverse AI repositories ($\bullet$ represents the mean value)
  • Figure 2: Code quality comparison of diverse and non-diverse AI repositories ($\bullet$ represents the mean value).
  • Figure 3: Ratio of multiple-authored, unknown-authored, male-authored and female-authored code files.
  • Figure 4: Code quality comparison of male-written and female-written code in multi-authored files.
  • Figure 5: Average of commit categories of female contributors
  • ...and 3 more figures