Women, artificial intelligence, and key positions in collaboration networks: Towards a more equal scientific ecosystem
This work addresses gender inequality in AI research collaboration, providing insights for a more equitable scientific ecosystem, though it is incremental in analyzing existing data.
The study investigated factors affecting acquisition of key positions in AI scientific collaboration networks from 2000 to 2019, finding that scientific performance is crucial for the 'social researcher' role regardless of gender, but subtle differences exist between female and male researchers in attaining the 'local influencer' role.
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.