Skip to content

Gender Diversity in AI Research

Lack of gender diversity in the artificial intelligence (AI) workforce is raising growing concerns. Our analysis shows that there is a gender diversity gap in AI research, in a larger and more comprehensive corpus than those which have been used to study this important issue before.

Key findings

  • There is a serious gender diversity crisis in AI research
    Only 13.83 per cent of authors are women and, in relative terms, the proportion of AI papers co-authored by at least one woman has not improved since the 1990s.
  • Location and research domain are significant drivers of gender diversity
    Women in the Netherlands, Norway and Denmark are more likely to publish AI papers while those in Japan and Singapore are less likely to. The UK is 22nd on this list, with 26.62 per cent of AI papers having at least one female co-author. Women working in physics, education, computer ethics and other societal issues, and biology, are more likely to publish work on AI in comparison to those working in computer science or mathematics.
  • There is a significant gender diversity gap in universities, big tech companies and other research institutions
    Apart from the University of Washington, every other academic institution and organisation in our dataset has less than 25 per cent female AI researchers. In big tech, only 11.3 per cent of Google’s employees who have published AI research on arXiv are women. The proportion is similar for Microsoft (11.95 per cent) and slightly better for IBM (15.66 per cent).
  • There are important semantic differences between AI papers with and without a female co-author
    When examining publications on machine learning and societal topics in the United Kingdom in 2012 and 2015, those involving at least one female co-author tend to be more semantically similar to each other than those without any female authors. Papers with at least one female co-author also tend to be more applied and socially aware, with terms such as fairness, human mobility, mental, health, gender and personality being among the most salient ones.

Our blog, How diverse is the workforce of AI research, looks in more detail at interviews we carried out with experts in the field. We discuss how our findings resonated with their experience in the sector and explore the cultural and institutional factors that determine gender diversity in AI research.

Authors

Konstantinos Stathoulopoulos

Konstantinos Stathoulopoulos

Konstantinos Stathoulopoulos

Principal Researcher, Innovation Mapping

Konstantinos is working as a Principal Researcher on Nesta's Research Analysis and Policy team.

View profile
Juan Mateos-Garcia

Juan Mateos-Garcia

Juan Mateos-Garcia

Director of Innovation Mapping

Juan leads a team of data scientists, developers, visualisers and innovation experts who use new datasets, analytics methods and visualisation tools to inform innovation policy.

View profile
Hannah Owen

Hannah Owen

Hannah Owen

Programme Coordinator, Education

Hannah works closely with Joysy John and across programmes to help prepare young people with the skills needed for the future

View profile