To give focus to ai To give women academics and others their well-deserved (and overdue) time in the spotlight, TechCrunch is launching an interview series focused on notable women who have contributed to the ai revolution. We will publish several articles throughout the year as the rise of ai continues, highlighting key work that often goes unnoticed. Read more profiles here.
As a reader, if you see a name we've overlooked and think should be on the list, email us and we'll try to add it. Here are some key people you should know:
The gender gap in ai
In a New York Times technology/ai-key-figures.html”>piece Late last year, the Gray Lady explained how the current ai boom came about, highlighting many of the usual suspects, such as Sam Altman, Elon Musk, and Larry Page. The journalism went viral, not because of what was reported, but because of what she did not mention: the women.
The Times list included 12 men, most of them leaders of technology or artificial intelligence companies. Many had no training or education, formal or otherwise, in ai.
Contrary to the Times' suggestion, the ai craze didn't start with Musk sitting next to Page in a mansion in the Bay Area. It started long before that, with academics, regulators, ethicists and hobbyists working tirelessly in relative obscurity to lay the foundation for the ai and GenAI systems we have today.
Elaine Rich, a retired computer scientist who previously worked at the University of Texas at Austin, published one of the first textbooks on ai in 1983 and then became director of a corporate ai laboratory in 1988. Harvard professor Cynthia Dwork caused a sensation for decades. she does in the fields of ai fairness, differential privacy, and distributed computing. And Cynthia Breazeal, a roboticist and professor at MIT and co-founder of robotics startup Jibo, worked to develop one of the first “social robots,” Kismet, in the late 1990s and early 2000s.
Despite the many ways women have advanced ai technology, they make up a small portion of the global ai workforce. According to a 2021 Stanford study ai-index-diversity-report-unmoving-needle”>studyonly 16% of permanent faculty focused on ai are women. ai/”>In a separate study Published the same year by the World Economic Forum, the co-authors find that women only hold 26% of positions related to analytics and artificial intelligence.
What's worse is that the gender gap in ai is widening, rather than narrowing.
Nesta, the UK's innovation agency for social good, carried out ai-less-14-ai-researchers-are-women-numbers-decreasing-over-last-10-years/”>an analysis of 2019 which concluded that the proportion of academic papers on ai co-authored by at least one woman had not improved since the 1990s. In 2019, only 13.8% of ai research articles on Arxiv.org, a repository of preprint scientific articles were written or co-authored by women, and the numbers had steadily declined over the previous decade.
Reasons for the disparity
The reasons for the disparity are many. But ai-en.pdf”>a Deloitte survey on women in ai highlights some of the most prominent (and obvious) ones, including judgment from male peers and discrimination as a result of not fitting into established male-dominated molds in ai.
It starts in college: 78% of women who responded to the Deloitte survey said they did not have the opportunity to intern in artificial intelligence or machine learning while they were college students. More than half (58%) said they ended up leaving at least one employer because men and women were treated differently, while 73% considered leaving the tech industry altogether due to pay inequality and an inability to advance. in their careers.
The lack of women is harming the field of ai.
Nesta's analysis found that women are more likely than men to consider the social, ethical and political implications in their work in ai, which is not surprising considering that women live in a world where they are disparaged for their gender, products The market has been designed for men, and women with children are often expected to balance work with their role as primary caregivers.
Hopefully, TechCrunch's humble contribution, a series about successful women in ai, will help move the needle in the right direction. But it is clear that there is much work to be done.
The women we profile share many suggestions for those who want to grow and improve the field of ai. But there is a common thread throughout: strong mentorship, commitment and leadership by example. Organizations can affect change by enacting policies (hiring, education, or otherwise) that elevate women already in the ai industry or looking to enter it. And decision makers in positions of power can wield that power to drive more diverse and supportive workplaces for women.
Change will not happen overnight. But every revolution begins with a small step.
