The AI Gender Gap
In the past few years, machine learning (ML) has become commercially successful and AI firmly established as a field. With its success, more attention is being paid specifically to the gender gap in AI. Compared to the general population, men are overrepresented in technology. While this has been the case for several decades, the opposite was true in the early days of computing when programming was considered a woman’s job.
Diversity has been shown to lead to good business outcomes like improved revenue. It’s also important that diverse voices are represented in the design of AI products which are used by large numbers of people.
Women in AI
Understanding the situation in the field of AI requires data. However it is hard to measure precisely the gender skew in AI as the workforce is global, fast growing, and highly mobile.
There are two recent studies estimating the gender gap in AI. Element AI’s Global talent report 2019 looks at who publishes in major AI research conferences and finds that 18% are female. Their 2018 report found 12% female participation. The second report — the World Economic Forum’s Gender Gap Report — found that 22% of the AI workforce is female.
Few companies publicise the proportion of female research staff they have working specifically on AI. A WIRED article found from their public research profiles that 10% of Google’s research workforce is female, and 15% of Facebook’s. Research Scientist is an important job category in industry as these roles are often the most coveted and highly paid.
These numbers are in line with the overall proportion of women working in technical roles in the UK. The IET currently estimate that 11% of the UK Engineering & technical workforce are female and the BCS estimate 17% of the UK IT workforce are women.
Career Progression
The overall numbers are not broken down by seniority. Research from the Anita Borg institute showed a 50% drop in women’s participation between entry and executive levels across the US technology industry. Similarly, the WEF report states:
“Male AI professionals are better represented in roles such as software engineer, head of engineering, head of IT as well as business owner and chief executive officer-positions that are generally more lucrative and of a more senior level.” — WEF Gender Gap Report
A separate report from Inclusive Boards found that in the UK:
“Almost two-thirds (65%) of boards in the top tech firms had no female directors. Over two-fifths of executive teams in the top tech firms had no female representation” — Inclusive Boards Tech Report 2018
The same report highlights the lack of overall diversity, with Black, Asian and Minority Ethnic (BAME) people making up just 8.5% of senior leaders in the UK tech industry.
These figures demonstrate how the proportion of women and minorities drops further at senior levels. This is important as senior employees typically have greater influence over product decisions and future directions.
Founding startups is another way that AI technology is being built. According to Tech Nation, investment in UK AI companies reached $1.3bn in 2018. Yet, research from the British Business bank showed that for every £1 of VC investment in the UK, 89p goes to all-male founding teams.
The Pipeline into AI
One reason for the lack of women in technical roles is the lack of girls choosing to pursue these careers. There are many paths into AI and a variety of jobs within the field. However, the highest paying AI jobs require a technical education, including some combination of maths, physics, engineering and computer science.
In the UK, A-levels are chosen at age 16 and are the first time that students can significantly narrow the choice of subjects they study. According to the Institute of Physics, around 20% of A-level Physics students are female and the Royal Society report that A-level Computing has a 9% take-up from girls. Girls accounted for 39% of this year’s maths A levels, and 28% of further maths A levels.
The figures continue through into undergraduate study at university. 17.6% of computer science university students in 2017/18 were female and 18.2% of Engineering students.
Conclusion
Together, these statistics paint a picture where women make up less than 20% of the AI field, dropping further at senior levels. In the UK, girls choose to study science and technology subjects at a lower rate than their male peers, both in secondary school and at undergraduate level. Leaving university, women choose not to pursue technology careers at the same rate as men. Women subsequently leave the technology workforce at a faster rate than men and face stronger headwinds when progressing their careers.
There are two important threads to addressing the gender gap in AI which must be tackled in parallel. The first is increasing the numbers of girls choosing to study STEM (Science, Technology, Engineering & Maths) subjects and follow these careers. The second is to better support women already in these careers so they can progress effectively. It is easier to build up initiatives to address the first of these, but progress will remain slow unless the second is addressed.
The statistics above have illuminated the gender gap in technology and AI. However, diversity means more than simply including women. More research into other minority groups is needed to see a fuller picture of who is working on AI, and who isn’t.
Update (20/07/2019): A couple of days after I wrote this post, Nesta published comprehensive research looking into the percentage of women authors of arXiv papers on AI topics. They found similar results to the two existing studies; 13.83 percent of the authors are women. They also looked deeper at large tech companies publishing on arXiv where the ratio is similar: Google (11.3%), Microsoft (11.95%) and IBM (15.66%). These figures show that the percentage of women working in the core research roles is lower than the percentage in all of the AI-related roles.
Originally published at http://mycomputerdoesntlisten.wordpress.com on July 14, 2019.
