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

TL;DR

This study examines gender disparities in scientific collaboration networks within artificial intelligence, revealing that performance influences key positions regardless of gender, but subtle gender differences exist in influencer roles.

Contribution

It provides a comprehensive analysis of gender effects on collaboration roles in AI using social network analysis, NLP, and machine learning from 2000 to 2019.

Findings

Performance impacts key network positions for all genders.

Subtle gender differences observed in local influencer roles.

Gender influences collaboration dynamics in AI research.

Abstract

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.