Empowering Female AI Academics to lead the way – TechCrunch launches a series of interviews focusing on exceptional women contributing to the AI revolution. We will be publishing several segments throughout the year as the wave of artificial intelligence continues to evolve, highlighting key works that are often unrecognized. Read more profiles here.
As a reader, if you see someone we missed and feel they should be on the list, please email us and we will try to add them. Here are some key figures to know:
The Gender Gap in AI
In a New York Times article at the end of last year, the cover story broke down how the current AI boom was created – and highlighted many of the usual suspects like Sam Altman, Elon Musk, and Larry Page. The news went viral – not because of what was reported, but because of what it didn't mention: women.
The "Times" list included 12 men – most of them AI or tech leaders. Many did not have formal training or education in artificial intelligence.
In contrast to the Times' assertion, the artificial intelligence craze did not start with Musk sitting next to Page in Silicon Valley. It began long before that, with academics, regulators, ethicists, and enthusiasts who worked relatively quietly to lay the groundwork for the AI and GenAI systems we have today.
Eileen Ritz, a computer scientist at former Texas A&M's Dimos and Harvard's Cynthia Dwork, who blazed trails in areas of artificial intelligence, differential privacy, and distributed computing. And Cynthia Breazeal, a roboticist and MIT professor and co-founder of Jibo, a robotics startup, worked on developing one of the earliest "social robots," Kismet, in the late 90s and early 2000s.
Despite the various ways in which women advanced artificial intelligence, they constitute a small part of the global AI workforce. According to a 2021 Stanford study, only 16% of the AI-focused faculty members are women. In a separate study published in the same year by the World Economic Forum, the co-authors find that women hold only 26% of analytics and AI roles.
In even worse news, the gender gap in AI is widening, not narrowing.
Nesta, the UK's innovation agency for societal good, conducted an analysis in 2019 that found the percentage of academic AI articles written by at least one woman has not improved since the 90s. As of 2019, only 13.8% of AI research articles on Arxiv.org, a repository for preprint scientific papers, were written or co-written by women, with the numbers steadily declining over the past decade.
Reasons for the Gaps
The reasons for the gap are many. But a survey by Deloitte among women in AI emphasizes some of the more prominent (and inherent) factors, including male peer judgment and discrimination resulting from not fitting into male-dominated templates in AI.
It starts in college: 78% of women who responded to Deloitte's survey said they did not have the opportunity to specialize in AI or machine learning while undergraduate students. More than half (58%) said they ultimately left at least one employer due to different treatment of men and women, while 73% considered leaving the tech industry altogether due to unequal pay and lack of career advancement.
The lack of women in AI is detrimental to the field.
Nesta's analysis found that women are more likely than men to consider the societal, ethical, and political implications of their AI work – not surprising considering women live in a world where their gender is belittled, products in the market are designed for men and women with children are expected to balance work with their primary caregiving role.
With a little luck, TechCrunch's humble contribution – a series on talented women in artificial intelligence – will help shift the needle in the right direction. But it's clear that there is a lot of work to be done.
The women we profile share many suggestions for those who want to grow and develop the field of artificial intelligence for the better. But a common thread runs through it all: strong mentorship, commitment, and leadership by example. Organizations can influence change by enacting policies – hiring, education, or otherwise – that elevate women already in the AI industry or interested in breaking into it. And decision-makers in positions of power can harness this power to push for more diverse workplaces and support for women.
Change won't happen overnight. But every revolution begins with a small step.
