March is Women's History Month in the US, the UK and Australia, a time to honour women's sometimes underrated contributions to society. According to the US National Women's History Museum, Women's History Month started in 1978 as a local "Women's History Week" celebration in California, with organizers selecting the week to correspond with the March 8 International Women's Day. The US Congress in 1987 passed Public Law 100-9 designating March as the Women's History Month. The past few decades have seen a steady increase in the number of women studying and excelling in the STEM fields. But this is not so in computer science -- the number of women studying or pursuing a career in computer science has been decreasing since around 1990.

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.

From improved medical applications to self-driving cars and smart cities, AI has the potential to transform our digital, physical, and social environments in unprecedented ways and at an unprecedented speed. However, the same technologies can be used for mass surveillance, computational propaganda, and biased, discriminating decision-making. It is generally believed that increasing the diversity of the workforce developing AI systems will reduce the risk that they generate discriminatory and unfair outcomes, thus ensuring that their benefits are more widely shared. But how diverse is the workforce of the AI sector? We conducted 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.