Gender-specific patterns in the artificial intelligence scientific ecosystem

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.

Automated summary

This study comprehensively analyzed gender-specific patterns in the field of artificial intelligence from 2000 to 2019 using social network analysis, natural language processing, and machine learning. The findings suggest an increasing rate of mixed-gender collaborations and a higher preference for female researchers to form homophilous collaborative links. The study also found a significant positive association between diverse collaborations and scientific performance and experience, and evidence supporting the rise of new female superstar researchers in the field of artificial intelligence.