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Scientific collaboration in almost every discipline is mainly driven by theneed of sharing knowledge, expertise, and pooled resources. Science is becomingmore complex which has encouraged scientists to involve more in collaborativeresearch projects in order to better address the challenges. As a highlyinterdisciplinary field with a rapidly evolving scientific landscape,artificial intelligence calls for researchers with special profiles covering adiverse set of skills and expertise. Understanding gender aspects of scientificcollaboration is of paramount importance, especially in a field such asartificial intelligence that has been attracting large investments. Usingsocial network analysis, natural language processing, and machine learning andfocusing on artificial intelligence publications for the period from 2000 to2019, in this work, we comprehensively investigated the effects of severaldriving factors on acquiring key positions in scientific collaboration networksthrough a gender lens. It was found that, regardless of gender, scientificperformance in terms of quantity and impact plays a crucial in possessing the"social researcher" in the network. However, subtle differences were observedbetween female and male researchers in acquiring the "local influencer" role.

Women, artificial intelligence, and key positions in collaboration networks: Towards a more equal scientific ecosystem