To shine a light on the often overlooked and underrepresented contributions of women in the AI revolution, TechCrunch is launching a series of interviews highlighting remarkable women academics and professionals in the field. As the AI boom continues, we will publish several pieces throughout the year, bringing recognition to the key work these women have done. Check out the previous profiles here.
If you know of a woman in AI who deserves to be on our list, please email us and we will strive to include them. Below are some of the notable individuals you should have on your radar.
The Gender Gap in AI
In a recent New York Times article, the publication delved into the origins of the current AI boom, featuring prominent figures such as Sam Altman, Elon Musk, and Larry Page. However, the piece went viral not for its content, but for what it left out – women.
The Times’ list consisted of 12 men, most of whom are leaders in the AI and tech industries. Many of these men lack formal education or training in AI.
Contrary to what the Times suggests, the AI craze did not start with a meeting between Musk and Page in a Bay Area mansion. It began long before that, with women in academia, regulatory bodies, ethics, and hobbyists working tirelessly behind the scenes to lay the groundwork for the AI and GenAI systems we have today.
Elaine Rich, a retired computer scientist formerly at the University of Texas at Austin, published one of the first AI textbooks in 1983 and later became the director of a corporate AI lab in 1988. Harvard professor Cynthia Dwork has been making waves in the fields of AI fairness, differential privacy, and distributed computing for decades. And Cynthia Breazeal, a roboticist and MIT professor, co-founded Jibo, a robotics startup, and developed one of the earliest “social robots,” Kismet, in the late 1990s and early 2000s.
Despite their significant contributions, women make up only a small fraction of the global AI workforce. According to a 2021 Stanford study, only 16% of tenure-track AI faculty are women. In a separate study released the same year by the World Economic Forum, it was found that women hold only 26% of positions related to analytics and AI.
Even more concerning, the gender gap in AI is not closing, but instead, widening.
In a 2019 analysis, Nesta, the UK’s innovation agency for social good, found that the percentage of AI research papers co-authored by women has not improved since the 1990s. As of 2019, only 13.8% of research papers on Arxiv.org, a repository for preprint scientific papers, were authored or co-authored by women, with the number steadily decreasing over the past decade.
Reasons for Disparity
There are numerous reasons for this disparity. A Deloitte survey of women in AI highlights some of the more prominent and obvious ones, including judgment from male peers and discrimination for not fitting into established male-dominated molds in AI.
The imbalance starts early on in college, with 78% of women respondents in the Deloitte survey reporting that they did not have the opportunity to intern in AI or machine learning during their undergraduate studies. More than half (58%) have left at least one employer due to unequal treatment between men and women, and 73% have considered leaving the tech industry altogether because of unequal pay and lack of advancement opportunities.
The lack of women in the AI field is hindering its progress.
The Nesta analysis also found that women are more likely than men to consider societal, ethical, and political implications in their work on AI – unsurprising given that women live in a world where they are belittled because of their gender, products are designed mostly for men, and working women with children often struggle to balance their careers and caregiving responsibilities.
With luck, TechCrunch’s contribution – a series on accomplished women in AI – will help move the needle in the right direction. However, there is still a lot of work to be done.
Advice for Progress
The women we profiled have many suggestions for those looking to move the AI field forward. A common thread among them is the importance of strong mentorship, dedication, and leading by example. Organizations can drive change by implementing policies – such as hiring practices and educational programs – to elevate women already in or aspiring to enter the AI industry. Decision-makers in positions of power can use their influence to promote more diverse and supportive workplaces for women.
Changing the current landscape won’t happen overnight, but every revolution begins with a small step.
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