Women are making a difference in AI


To deliver AI-centric Following the well-deserved – and overdue – spotlight on female academics and others, TechCrunch is launching a series of interviews focusing on notable women who have contributed to the AI ​​revolution. As the AI ​​boom continues, we'll be publishing several pieces throughout the year, highlighting key work that often goes unrecognized. Read more profiles here.

As a reader, if you see any names we've forgotten and you think should be on the list, please email us and we'll try to add them. Here are some of the key people you should know about:

Gender gap in AI

In a New York Times article late last year, the Gray Lady explained how the current surge in AI came about – highlighting many of the usual suspects like Sam Altman, Elon Musk, and Larry Page. Journalism went viral – not for what it reported, but for what it failed to mention: women.

The Times list includes 12 people — most of them leaders of AI or tech companies. Many had no training or education in AI, formal or otherwise.

Contrary to what the Times suggests, the AI ​​craze didn't start with Musk sitting next to Page in a mansion on the bay. It started long before that, with academics, regulators, ethicists, and hobbyists working tirelessly in relative obscurity to build the foundation for the AI ​​and GenAI systems we have today.

Ellen Rich, a former retired computer scientist at the University of Texas at Austin, published one of the first textbooks on AI in 1983, and later became the director of a corporate AI lab in 1988. Harvard professor Cynthia Dwork made waves decades ago in the fields of AI fairness, differential privacy, and distributed computing. And Cynthia Brazile, a roboticist and professor at MIT and co-founder of the robotics startup Jibo, worked to develop one of the earliest “social robots,” Kismet, in the late '90s and early 2000s.

Despite women having advanced AI technology in many ways, they constitute a small portion of the global AI workforce. According to a 2021 Stanford study, only 16% of tenure-track faculty focusing on AI are women. In a separate study released the same year by the World Economic Forum, co-authors found that women hold only 26% of analytics-related and AI positions.

The worse news is that the gender gap in AI is growing, not shrinking.

Nesta, the UK's innovation agency for social good, conducted an analysis in 2019 which concluded that the proportion of AI academic papers co-written 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 been steadily declining over the past decade.

due to inequality

There are many reasons for inequality. But the Deloitte survey of women in AI highlights some of the more prominent (and obvious) things, including judgment from male peers and discrimination as a result of not fitting into the established male-dominated framework in AI.

It starts in college: 78% of women responding to the Deloitte survey said they didn't have the opportunity to do an internship in AI or machine learning when they were undergraduates. More than half (58%) said they left at least one employer because men and women were treated differently, while 73% said they left an employer because of unequal pay and the inability to advance in their careers. Considered leaving the industry altogether.

The lack of women is hurting the AI ​​field.

Nesta's analysis found that women are more likely than men to consider the social, ethical and political implications in their work on AI – which is not surprising given that women live in a world where they are more likely to be criticized for their gender, products, Is insulted on this basis. The market is designed for men, and women with children are often expected to balance work with their role as primary caregiver.

Luckily, TechCrunch's humble contribution – a series on women adept at AI – will help move the needle in the right direction. But clearly there is still a lot of work to be done.

The women we profile share several tips for those who want to improve and grow the AI ​​field. But a common thread runs throughout: strong guidance, commitment, and leading by example. Organizations can affect change by implementing policies – in 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 use that power to push for more diverse, supportive workplaces for women.

Change will not happen overnight. But every revolution starts with a small step.