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与不同的问题解决方式相关联，以及如何消除发现的任何此类偏见。OSS正在对社会产生重大影响，包括它所生产的产品和它所促进的职业道路。然而，女性在OSS开发人员中的代表性远远不足。对于OSS社区来说，这是一个重要的问题，因为它阻止了他们获得更大的人才库的好处，以及团队多样性带来的好处。此外，问题还会自生自弃：女性开发人员会错过OSS项目提供的学习和专业成长机会，而当开源贡献被用来做招聘决定时，职业机会又会避开女性开发人员，这两者都阻碍了她们参与OSS。这项工作将利用基础性别研究来提供基于理论但实用的解决方案，以解决女性在OSS社区中代表性不足的问题。在第一阶段，研究将通过GenderMag研究技术对性别偏见进行实验识别，并对结果进行实证交叉验证。这是一种软件专业人员用来在他们自己的软件中发现性别偏见“bug”的检查方法，通过评估他们的软件在五个方面支持不同认知风格的程度：(1)动机，(2)计算机自我效能，(3)技术风险规避，(4)信息处理风格，(5)学习新技术的风格。从阶段1中确定的bug所涉及的每个方面开始，阶段2的研究将通过与团队密切合作，对确定的每个方面进行重新设计。重新设计和创建包容性工具的过程将经过经验评估，以创建修复性别偏见错误的“最佳实践”汇编，包括产品（对此类错误的合适修复）和过程（OSS团队如何一起工作以修复性别偏见错误）。持续评估将是贯穿始终的关键组成部分，以确定哪些实践和过程有效，哪些无效。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值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.