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.
翻译:隐含的性别偏见在软件开发中是一个被广泛讨论的问题,例如将技术性的角色与男性联系起来等。为了解决这种偏见,理解更多细节变得至关重要。本研究利用数据挖掘技术调查56个与软件开发相关的任务,在大型语言模型中嵌入隐含性别偏见的程度。我们系统地将每个任务从英语翻译成无性别语言,然后再翻译回来,并调查每个任务所相关联的代词。基于对不同排列方式下的每个任务进行100次翻译,我们发现不同任务中与性别有关联的代词具有明显的差异。具体而言,只有6%的情况下,将需求收集与代词"她"相关联,而将测试与代词"他"相关联的概率则达到了100%。此外,与帮助他人相关的任务有91%的关联代词是"他",而与询问同事相关的任务则只有52%。这些发现揭示了软件开发任务与性别偏见的明显模式,并对在大型语言模型的训练和更广泛的社会应对这个问题都有重要意义。