According to a recent report by the World Economic Forum, a mere 22 percent of the workforce in Artificial Intelligence is comprised of women. In Australia, that figure is even lower. There’s a gap, even, in the qualified versus employed: 55 percent of STEM graduates are women; they’re just not moving into the field at the rate that men are. Across most studies worldwide, the figure for women in data science is around 15 to 22 per cent. So how do we get more women into STEM and data science? Who’s leading the way on this front, and what can we learn from them?
In a recent report from the UNESCO Institute for Statistics, Central Asia as well as Latin America and the Caribbean are approaching gender parity in STEM, with the highest share of women working in research and development (R&D), at 45 per cent. That’s compared to only one-third of R&D employees in Europe and North America. Azerbaijan and North Macedonia are also ahead of the curve, at 60 per cent female researchers. Some nations have made great strides over time, such as South Korea, which has seen a boost in female researchers from 10 per cent to 19 per cent since 2000; and Morocco, which has gone from 26 per cent to 34 per cent.
So, what can be done to learn from these countries and close the gap worldwide?
In some nations, such as China, maths education is required for all students beginning in primary school and continuing through high school. Girls are introduced to STEM early on, and so become more likely to pursue careers in it upon entering university. In Australia, the Early Learning STEM Australia Pilot began in 2018 and prioritises access and equity for vulnerable schools across the country. Details of the pilot can be found here.
Create gender quotas
Starting at the top will have a trickle-down effect which benefits all. Companies worldwide now have quotas for female representation at board level, and similar strides can be made in the sciences.
Offer support and incentives
One issue preventing more women from rising to the top of STEM statistics is the “leaky pipeline” problem: women who become scientists often do not stay scientists. This may be due to company policy more than women’s deliberate choices on the matter, as many companies don’t offer proper maternity leave or part time options catering to different lifestyles.
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Data science still has the problem of appearing “abstract and without a purpose,” which can turn some candidates, including women, away from the field. Despite the fact that data science is what supports many of the technologies we use in our daily lives–from Google search to smart phones–many people see it as far removed from practical application. In a recent BCG study, only half of students surveyed agreed that data scientists spend their time solving real-life problems: “For 49% of students overall (48% of women, 50% of men) in the countries surveyed, the field is instead seen to be theoretical and abstract, focused on manipulating code and data with low impact and, by implication, low purpose.”
However, it could be argued that some of these statistics reinforce existing stereotypes, pegging women as primarily interested in human-centered jobs and men more interested in data-driven jobs. We should be careful in interpreting any of these studies, as even the BCG survey only reflects a preconceived notion about the field and not students’ interest in it.