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

TL;DR

This study empirically investigates how gender diversity in AI development teams positively affects repository popularity, code quality, and individual contributions, emphasizing the importance of increasing female participation for better AI systems.

Contribution

It provides the first empirical analysis of gender diversity's impact on AI code quality, project popularity, and individual contributions, filling a significant research gap.

Findings

Diverse AI repositories have higher popularity and community engagement.

Repositories with gender diversity tend to have superior code quality.

Female contributors, though fewer, produce higher quality contributions.

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.