Gender-Specific Patterns in the Artificial Intelligence Scientific Ecosystem

TL;DR

This study analyzes gender-specific collaboration patterns in AI research from 2000 to 2019, revealing increasing mixed-gender collaborations, disciplinary homophily, and the potential rise of female superstar researchers.

Contribution

It provides a comprehensive, data-driven analysis of gender dynamics in AI research using social network analysis, NLP, and machine learning, highlighting new collaboration trends and disparities.

Findings

Increasing mixed-gender collaborations over time

Higher gender homophily among female researchers

Diverse collaborations correlate with higher scientific performance

Abstract

Gender disparity in science is one of the most focused debating points among authorities and the scientific community. Over the last few decades, numerous initiatives have endeavored to accelerate gender equity in academia and research society. However, despite the ongoing efforts, gaps persist across the world, and more measures need to be taken. Using social network analysis, natural language processing, and machine learning, in this study, we comprehensively analyzed gender-specific patterns in the highly interdisciplinary and evolving field of artificial intelligence for the period of 2000-2019. Our findings suggest an overall increasing rate of mixed-gender collaborations. From the observed gender-specific collaborative patterns, the existence of disciplinary homophily at both dyadic and team levels is confirmed. However, a higher preference was observed for female researchers to form homophilous collaborative links. Our core-periphery analysis indicated a significant positive association between having diverse collaboration and scientific performance and experience. We found evidence in support of expecting the rise of new female superstar researchers in the artificial intelligence field.