Providing AI-centered services At a time when women scholars and others are receiving a deserved and overdue spotlight, TechCrunch is launching a series of interviews focusing on the extraordinary women who have contributed to the AI revolution. We will publish several articles throughout the year highlighting key work that often goes unrecognized as the AI boom continues. Read more profiles here.
As a reader, if you think there's a name we've missed and should be on the list, please email us and we'll add it. Here are some key people you need to know:
Gender Gap in AI
In a New York Times article late last year, the Gray Lady analyzed how the current AI boom came about, highlighting the usual suspects: Sam Altman, Elon Musk, and Larry Page. Journalism went viral not because of what was reported, but because of what was not said: women.
The Times' list included 12 men, most of whom were leaders of AI or technology companies. Many people have no training or education about AI, formal or otherwise.
Contrary to what the Times suggests, the AI craze didn't start when Musk sat next to Page in a mansion in the Bay. It started a long time ago, when academics, regulators, ethicists, and enthusiasts worked tirelessly in relatively obscure environments to build the foundation for the AI and GenAI systems we have today.
Elaine Rich, a retired computer scientist at the University of Texas at Austin, published one of the first textbooks on AI in 1983 and later became director of the Corporate AI Research Institute in 1988. Harvard professor Cynthia Dwork has been making groundbreaking advances for decades. Previously, she worked in the areas of AI fairness, differential privacy, and distributed computing. And Cynthia Breazeal, a roboticist, MIT professor, and co-founder of robotics startup Jibo, helped develop Kismet, one of the first “social robots,” in the late '90s and early 2000s.
Despite the many ways women have advanced AI technology, they make up a very small portion of the global AI workforce. According to a 2021 Stanford study, only 16% of full-time faculty focused on AI are women. In a separate study published by the World Economic Forum that same year, the co-authors found that only 26% of analytics and AI positions were held by women.
The worse news is that the gender gap in AI is widening, not narrowing.
A 2019 analysis by Nesta, the UK's social care innovation agency, concluded that the proportion of AI academic papers co-authored by at least one woman has not improved since the 1990s. As of 2019, only 13.8% of AI research papers on Arxiv.org, a repository of preprint scientific papers, were authored or co-authored by women, a number that has steadily declined over the past decade.
Reason for the discrepancy
The reasons for the gap are varied. But Deloitte's survey of women in AI highlights some of the more notable (and obvious) points, including judgment from male colleagues and discrimination due to not fitting AI's established male-dominated framework. .
It starts in college. 78% of women who responded to a Deloitte survey said they did not have the opportunity to intern in AI or machine learning during their undergraduate years. More than half (58%) say they have left at least one employer because of the way men and women are treated differently, and 73% have considered leaving the tech industry altogether due to unequal pay and lack of ability to advance their careers.
The lack of women is harming the AI field.
Nesta's analysis found that women are more likely than men to consider the social, ethical and political implications of their AI work. This is not surprising considering that we live in a world where women are looked down upon based on their gender and products. The market is designed for men and women with children, who often have to balance work with their role as primary caregivers.
With any luck, TechCrunch's humble contribution to our series about extraordinary women in AI will help move the needle in the right direction. But clearly there is a lot of work to be done.
The women we feature share many suggestions for those who want to grow and advance the AI field for the better. But the common thread is strong mentorship, commitment, and leadership by example. Organizations can affect change by enacting policies (recruitment, training, etc.) that elevate the status of women already working in the AI industry or seeking to enter it. And decision-makers in positions of power can use that power to promote more diverse and supportive workplaces for women.
Change doesn't happen overnight. But every revolution starts with small steps.