CHS: Large: Collaborative Research: Gender-Inclusive Open Source through Gender-Inclusive Tools

这项研究将调查开源软件(OSS)工具和技术是否以及如何将性别偏见与不同的问题解决方式联系在一起，以及如何消除发现的任何此类偏见。开放源码软件正在对社会产生重大影响，它生产的产品和它促进的职业道路。然而，在开放源码软件开发人员中，女性的比例严重不足。这是开放源码软件社区的一个重大关切，因为它使他们无法从更大的人才库中获得好处，也无法从团队多样性中获得好处。此外，问题本身就是：女性开发人员错过了开放源码软件项目提供的学习和职业成长机会，当利用开放源码贡献做出招聘决策时，职业机会避开了女性开发人员，这两者都阻碍了她们参与开放源码软件。这项工作将利用基础性的性别研究，提供基于理论但实用的解决方案，以解决开放源码软件社区中女性代表不足的问题。在第一阶段，研究将通过GenderMag研究技术试验性地识别性别偏见，并对结果进行经验性交叉验证。这是一种软件专业人员通过评估他们的软件在五个方面对不同认知风格的支持程度的检查方法，用来发现他们自己软件中的性别偏见“缺陷”：(1)动机，(2)计算机自我效能，(3)对技术的风险厌恶，(4)信息处理风格，(5)学习新技术的风格。从第一阶段确定的Bug中涉及的每个方面开始，第二阶段的研究将通过与团队的密切合作来为确定的每个方面进行重新设计。将对重新设计和创建包容性工具的过程进行经验性评估，以创建一份关于修复性别偏见错误的“最佳做法”的简编，在产品(对此类错误的适当修复是什么)和过程(开放源码软件团队如何合作修复性别偏见错误)中都是如此。持续的评估将是整个过程中的关键组成部分，以确定哪些实践和过程有效，哪些无效。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

This research will investigate whether and how open source software (OSS) tools and technologies have gender biases tied with diverse problem-solving styles, and how to remove any such biases that are found. OSS is having a significant impact on society, in the products it produces and the career paths that it facilitates. However, women are vastly underrepresented among OSS developers. This is a significant concern to OSS communities because it prevents them from receiving the benefits of a larger talent pool and the benefits that ensue from team diversity. Further, the problem feeds upon itself: women developers then miss out on the learning and professional growth opportunities that OSS projects provide, and professional opportunities evade women developers when open source contributions are used to make hiring decisions, both of which hold them back from OSS engagement.This work will harness foundational gender research to provide theory-based yet practical solutions, towards addressing the underrepresentation of women in OSS communities. In Phase 1, the research will experimentally identify gender biases through the GenderMag research technique, and cross-validate results empirically. This is an inspection method for software professionals to use to find gender-bias "bugs" in their own software, by assessing to what extent their software supports diverse cognitive styles in terms of five facets: (1) motivations, (2) computer self-efficacy, (3) risk aversion with technology, (4) information processing styles, and (5) styles of learning new technology. Starting with each facet implicated in the bugs identified in Phase 1, the Phase 2 research will derive redesigns for each facet identified by working closely with the teams. The redesigns and the process of creating inclusive tools will be empirically evaluated to create a compendium of "best practices" for fixing gender-bias bugs, in both products (what suitable fixes are to such bugs) and processes (how OSS teams can work together to fix gender-bias bugs). Continuous evaluation will be a critical component throughout, to determine which practices and processes work and which do not.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.