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

我的研究涉及人工智能(AI)研发中的开放的社会动力，特别关注开放源代码软件共享和开发的社会和技术网络。我的研究将把社会理论与一套数据科学方法结合起来，通过对这一主题的三项独立但相互关联的研究，来探究这个实践社区中不均衡的参与和影响。第一项研究将考察开源人工智能软件开发在软件托管平台GitHub上的社会和技术网络结构，因为它很受欢迎，并且通过其公共API提供数据。在收集和准备开发人员和软件项目之间的数据记录联系后，我将使用网络科学技术来检查人工智能软件项目中协作的网络结构，以及谁和谁的软件已经成为这个社区的中央权威。这项研究将有助于学术研究科技领域的社交网络，以及旨在提高人们对人工智能领域不均衡参与和影响的认识的实际成果。第二项研究将考察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。同样，我将以编程方式收集关于软件开发活动的地理编码数据，并按国家列举这些活动。我将使用地图分析来绘制全球活动图，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。这一分析将为针对各国政府和国际组织的政策建议提供信息，这些建议旨在如何扩大全球对人工智能社区的参与。第三项研究将聚焦于少数从业者，在半结构化访谈中，他们将被问及他们认为谁的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。使用有目的的抽样方法，我将对第一项研究中确定的用户进行访谈。这项研究将有助于对这个实践的科学共同体成员的想象力的学术理论。总体而言，该项目将有助于学院在软件开发方面的不平等问题上的工作，并为旨在扩大在这一日益重要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.