  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.
