«Summary
Lack of gender diversity in the Artificial Intelligence (AI) workforce is raising
growing concerns, but the evidence base about this problem has until now
been based on statistics about the workforce of large technology companies or
submissions to a small number of prestigious conferences.
We build on this literature with a large-scale analysis of gender diversity in AI
research using publications from arXiv, a widely-used preprints repository where we
have identified AI papers through an expanded keyword analysis, and predicted
author gender using a name-to-gender inference service. We study the evolution
of gender diversity in various disciplines, countries and institutions, finding that
while the share of female co-authors in AI papers is increasing, it has stagnated
in disciplines related to computer science. We also find that geography plays an
important role in determining the share of female authors in AI papers and that
there is a severe gender gap in the top research institutions. We also study the link
between female authorship in papers and the citations it receives, finding a strong,
positive correlation in research domains related to the impact of information
technology on society. Having done this, we examine the semantic differences
between AI papers with and without female co-authors. Our results suggest that
there are significant differences in machine learning and computer ethics between
the United States and the United Kingdom as well as differences in the research
focus of papers with female co-authors. We conclude by reporting the results of
interviews with female AI researchers and other important stakeholders aimed at
interpreting our findings and identifying policies to improve diversity and inclusion
in the AI research workforce».
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