Scientific collaboration in almost every discipline is mainly driven by the need of sharing knowledge, expertise, and pooled resources. Science is becoming more complex which has encouraged scientists to involve more in collaborative research projects in order to better address the challenges. As a highly interdisciplinary field with a rapidly evolving scientific landscape, artificial intelligence calls for researchers with special profiles covering a diverse set of skills and expertise. Understanding gender aspects of scientific collaboration is of paramount importance, especially in a field such as artificial intelligence that has been attracting large investments. Using social network analysis, natural language processing, and machine learning and focusing on artificial intelligence publications for the period from 2000 to 2019, in this work, we comprehensively investigated the effects of several driving factors on acquiring key positions in scientific collaboration networks through a gender lens. It was found that, regardless of gender, scientific performance in terms of quantity and impact plays a crucial in possessing the "social researcher" in the network. However, subtle differences were observed between female and male researchers in acquiring the "local influencer" role.

本文使用社交网络分析、自然语言处理和机器学习，重点关注2000年至2019年的人工智能出版物，全面调查了几个驱动因素对在科学合作网络中获得关键位置的影响，发现科学表现在数量和影响方面对于在网络中具有“社交研究员”角色至关重要 。然而，女性研究人员和男性研究人员在获得“地方影响者”角色方面存在微小差异。

女性、人工智能和协作网络中的重要职位：迈向更平等的科学生态系统