Participation and Practices in Open Artificial Intelligence: A study of the Sociotechnical Networks of Open-Source Software Sharing and Development

我的研究关注人工智能（AI）研究和开发中开放性的社会动态，特别关注开源软件共享和开发的社会和技术网络。我的研究将社会理论与一套数据科学方法相结合，以探讨在这个实践社区中的不均衡参与和影响，通过三个独立但相互关联的研究来实现。第一项研究将研究软件托管平台GitHub上开源AI软件开发的社会和技术网络结构，因为它的流行程度和通过其公共API的数据可用性。在收集和准备开发人员和软件项目之间的数据记录关系时，我将使用网络科学技术来研究人工智能软件项目中的协作网络结构，以及谁和谁的软件已经成为这个社区的中心权威。该研究将有助于科学和技术领域的社交网络的学术研究，以及提高对人工智能领域不平衡参与和影响的认识的实际产出。第二项研究将研究开源人工智能软件开发的地理轮廓和集中度，以及可以解释全球差异的因素。类似地，我将以编程方式收集关于软件开发活动的地理编码数据，并以国家为基础列举这些活动。我将使用地图分析来绘制全球活动，并使用多元回归分析来分析哪些因素可以解释国家之间的差异。该分析将为各国政府和国际组织提供关于如何扩大全球参与人工智能社区的政策建议。第三项研究将聚焦于少数从业者，他们将在半结构化访谈中被问及他们认为哪些价值观和兴趣会影响开源AI软件社区的规范、依赖关系和可能的未来。使用目的抽样的方法，我将进行访谈的用户在第一次研究中确定。本研究将有助于对这一科学实践共同体成员的资格的学术理论。总体而言，该项目将有助于该学院在软件开发不平等方面的工作，并为旨在扩大这一日益关键的领域的参与的决策提供信息。

My research concerns the social dynamics of openness in artificial intelligence (AI) research and development, with a particular focus on the social and technical networks of open source software sharing and development. My research will blend social theory with a suite of data science methods to interrogate uneven participation and influence in this community of practice, operationalised through three separate but interrelated studies on this subject matter. The first study will examine the social and technical network structure of open source AI software development on the software hosting platform GitHub, due to its popularity and data availability via its Public API. Upon the collection and preparation of data recording ties between developers and software projects, I will use network science techniques to examine the network structure of collaboration in AI software projects as well as who and whose software have emerged as central authorities in this community. The study will contribute to academic scholarship of social networks in science and technology fields as well as practical outputs oriented at raising awareness of uneven participation and influence in the field of AI. The second study will examine geographic contours and concentrations of open source AI software development and the factors that can explain global disparities. Similarly, I will programmatically collect geo-coded data on software development activities and enumerate these activities on a country-basis. I will use cartographic analysis to map global activity and use multivariate regression analysis to analyse what factors can explain disparities between countries. This analysis will inform policy recommendations targeted at national governments and international organisations on how to broaden global participation in the AI community. The third study will zoom into a handful of practitioners who in semi-structured interviews will be asked whose values and interests do they perceive to influence norms, dependencies, and possible futures in the open source AI software community. Using purposive sampling methods, I will conduct interviews with users identified in the first study. This study will contribute to academic theory on the imaginaries of members of this scientific community of practice. Overall, this project will contribute to work in the academy on inequality in software development as well as inform policymaking aimed at widening participation in this increasingly pivotal field.