Women in AI making a difference

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To give an AI-driven approach to women in academia and others for their well-deserved – and overdue – time in the spotlight, TechCrunch is launching a interview series focusing on remarkable women who have contributed to the AI ​​revolution. We’ll be publishing several articles throughout the year as the AI ​​boom continues, highlighting key work that often remains overlooked. Read more profiles here.

As a reader, if you see a name we missed and think it should be on the list, please email us and we’ll look into adding it. Here are some key people you should know:

The gender gap in AI

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

The Times list included 12 men, most of them executives from AI or technology 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 Bay Area mansion. It started long before that, with academics, regulators, ethicists, and amateurs working tirelessly in relative obscurity to lay the foundations for the AI ​​and GenAI systems we have today.

Retired computer scientist Elaine Rich, formerly of the University of Texas at Austin, published one of the first AI textbooks in 1983, then became director of a corporate AI lab in 1988. Harvard professor Cynthia Dwork has been making waves for decades. there are in the areas of AI fairness, differential privacy and distributed computing. And Cynthia Breazeal, roboticist and professor at MIT and co-founder of For athe robotics startup, 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 represent only a tiny portion of the global AI workforce. According to a Stanford 2021 studyonly 16% of full professors focused on AI are women. In a separate study published the same year by the World Economic Forum, the co-authors find that women occupy only 26% of positions related to analytics and AI.

Worse yet, the gender gap in AI is widening, not narrowing.

Nesta, the UK’s innovation agency for social good, led a 2019 analysis which concluded that the proportion of academic articles on AI co-authored by at least one woman had not improved since the 1990s. In 2019, only 13.8% of research articles on AI published on Arxiv.org, a repository of pre-print scientific articles, were authored or co-authored by women, with the number steadily declining over the previous decade.

Reasons for the disparity

The reasons for this disparity are numerous. But a Deloitte survey of women in AI highlights some of the most important (and obvious) ones, including judgment from male peers and discrimination resulting from not fitting into the established male-dominated molds of AI.

It starts in college: 78% of women responding to Deloitte’s survey said they didn’t have the chance to intern in AI or machine learning during their undergraduate studies. More than half (58%) said they ended up leaving at least one employer because of how men and women were treated differently, while 73% considered leaving the tech industry altogether due to the wage inequality and the inability to advance in their careers.

The lack of women is hurting the field of AI.

Nesta’s analysis found that women are more likely than men to consider the societal, ethical and political implications of their AI work – which is unsurprising given that women live in a world where they are demeaned because of their gender, products have been designed for men, and women with children are often expected to balance their work and their role as primary caregivers.

Hopefully, TechCrunch’s humble contribution – a series about accomplished women in AI – will help move things in the right direction. But there is clearly a lot of 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 a common thread is present throughout: strong mentoring, commitment and example. Organizations can influence change by adopting policies (hiring, training or others) that promote women already present or seeking to break into the AI ​​sector. And decision-makers in positions of power can wield that power to promote more diverse and women-friendly workplaces.

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

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