To give female academics and others focused on AI their well-deserved and overdue spotlight time, TechCrunch is launching a series of interviews highlighting notable women who have contributed to the AI revolution. Start. As the AI boom continues, we'll be publishing several articles throughout the year highlighting key research that may go unrecognized. Click here for a detailed profile.
As a reader, if you see a name we missed and think it should be on the list, please email me. I will try to add more. Here are the key players you should know about.
Eileen Solaiman, Head of Global Policy, Hugging Face Eva Meidel, Member of the European Parliament, EU AI Legal Adviser Lee Tiedrich, AI Expert at the Global Partnership on AI Rashida Richardson, Senior Advisor at Mastercard on AI and Privacy Focused on Crystal Kaufman, Researcher at the Distributed AI Institute Amba Kaku creates policy recommendations to address AI concerns Miranda Bogen creates solutions to help manage AI Mutare Nkonde's nonprofit works to reduce bias in AI Karine Perset helps governments understand AI Francine Bennett uses data science to make AI more accountable Sarah Krebs, Professor of Government, Cornell University Sandra Wachter, Professor of Data Ethics, University of Oxford Claire Leibowitz, PAI AI and Media Integrity Expert Heidi Klaf, Director of Safety Engineering at Trail of Bits Tara Chiklovsky, CEO of Technovation and Founder Katherine Breslin, Founder, Kingfisher Labs Director Rachel Coldicutt, Founder, Careful Industries Dar'shun Kendrick, Georgia State Representative
Gender gap in AI
In a New York Times article late last year, Gray Lady highlighted many of the usual suspects, including Sam Altman, Elon Musk, and Larry Page, to explain how the current AI boom was born. was explained in detail. Journalism took off not because of what was reported, but because of what women were not mentioned.
The paper's list included 12 men, most of them leaders of AI and technology companies. Many had no training or education in AI, formal or otherwise.
Contrary to what the Times suggests, the AI boom didn't start with Mr. Musk sitting next to Mr. Page in Bay's mansion. It started long before that, when academics, regulators, ethicists, and hobbyists worked tirelessly in relative obscurity to build the foundation for the AI and generative AI systems we have today. .
Elaine Rich, a former computer scientist at the University of Texas at Austin, published one of the first textbooks on AI in 1983 and went on to become director of the company's AI research lab in 1988. Harvard professor Cynthia Dwark has been making headlines for decades. Previously she worked in the areas of AI fairness, differential privacy, and distributed computing. And Cynthia Breazeale, a roboticist and professor at MIT and co-founder of robotics startup Jibo, says that her work on the earliest “social robots” began in the late '90s and early 2000s. We worked on the development of Kismet, which is one of these.
Women are leveraging advanced AI technologies in a variety of ways, but they make up a small portion of the global AI workforce. According to a 2021 Stanford University study, only 16% of tenure-track faculty specializing in AI are women. In another study published the same year by the World Economic Forum, co-authors found that only 26% of analytics and AI jobs were held by women.
The worse news is that the gender gap in AI is widening, not narrowing.
Nesta, the UK's Social Good and Innovation Agency, conducted a 2019 analysis and 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 her AI research papers on Arxiv.org, a repository of preprint scientific papers, were authored or co-authored by women, the highest number in the past 10 It has been steadily decreasing over the years.
Reasons for disparity
There are many reasons for the disparity. But her Deloitte study of women in AI found some of the more salient (and obvious) issues, including judgment from male colleagues and discrimination for not fitting the established mold of male dominance in AI. It is embossed.
It starts at university. Seventy-eight percent of women who responded to Deloitte's 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 ended up leaving at least one employer because men and women are treated differently, and 73% have left the technology industry because of unequal pay and lack of career advancement. He replied that he was considering leaving the company completely.
The lack of women is having a negative impact on the AI field.
Nesta's analysis found that women are more likely than men to consider social, ethical and political implications in AI research. This is not surprising given that we live in a world where women are looked down upon because of their gender or products. The market is designed for men and women with children, who are often expected to balance work with their roles as primary caregivers.
With any luck, TechCrunch's small contribution – a series on outstanding 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 evolve the AI field for the better. But common elements run throughout. It is strong leadership, dedication, and leading by example. Organizations can make a difference by enacting employment, education, and other policies that empower women already in or considering joining the AI industry. And decision-makers in positions of power can use their authority to promote a more diverse and supportive workplace for women.
Change doesn't happen overnight. But every revolution starts with a small step.