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Women in AI are making a difference

To give AI-focused women academics and others their well-deserved – and overdue – time within the highlight, TechCrunch is launching a series of interviews specializing in notable women who’ve contributed to the AI ​​revolution. As the AI ​​boom continues, we are going to publish several articles all year long highlighting necessary work that usually goes unrecognized. You can find more profiles here.

If you as a reader see a reputation that we missed that must be on the list, please email me and I’ll try so as to add it. Here are some necessary people you must know:

  • Irene Solaiman, Head of Global Policy at Hugging Face
  • Eva Maydell, Member of the European Parliament and advisor on the EU AI law
  • Lee Tiedrich, AI expert on the Global Partnership on AI
  • Rashida Richardson, Senior Counsel at Mastercard focused on AI and data protection
  • Krystal Kauffman, research associate on the Distributed AI Research Institute
  • Amba Kak creates policy recommendations to deal with AI concerns
  • Miranda Bogen develops solutions to regulate AI
  • Mutale Nkonde's non-profit organization works to make AI less biased
  • Karine Perset helps governments understand AI
  • Francine Bennett uses data science to make AI more responsible
  • Sarah Kreps, professor of presidency at Cornell
  • Sandra Wachter, Professor of Data Ethics at Oxford
  • Claire Leibowicz, AI and media integrity expert at PAI
  • Heidy Khlaaf, Director of Security Engineering at Trail of Bits
  • Tara Chklovski, CEO and founding father of Technovation
  • Catherine Breslin, Founder and Director of Kingfisher Labs
  • Rachel Coldicutt, founding father of Careful Industries
  • Representative Dar'shun Kendrick, member of the Georgia House of Representatives

The gender gap in AI

In a New York Times Piece Late last yr, the Gray Lady explained how the present AI boom got here about – highlighting lots of the usual suspects like Sam Altman, Elon Musk and Larry Page. The journalism went viral not due to what it reported, but due to what it didn't mention: women.

The Times' list included 12 men – most of them executives at AI or technology firms. Many had no formal or other education or training in AI.

Contrary to the Times' claim, the AI ​​madness didn't start with Musk sitting next to Page in a bayside mansion. It began long before that, when academics, regulators, ethicists, and hobbyists worked tirelessly and in relative obscurity to put the foundations for the AI ​​and generative AI systems now we have today.

Elaine Rich, a retired computer scientist who formerly worked on the University of Texas at Austin, published one among the primary textbooks on AI in 1983 and have become head of a company AI lab in 1988. Harvard professor Cynthia Dwork caused a stir for a long time within the areas of AI fairness, differential privacy and distributed computing. And Cynthia Breazeal, a roboticist and professor at MIT and co-founder of the robotics startup Jibo, worked on developing one among the earliest “social robots,” Kismet, within the late ’90s and early 2000s.

Despite the various ways through which women have advanced AI technology, they represent only a tiny portion of the worldwide AI workforce. According to a 2021 Stanford studyonly 16% of tenure-track faculty focused on AI are women. In a separate study In a study published by the World Economic Forum that very same yr, the co-authors found that girls hold only 26% of analytics and AI positions.

What’s worse is that the gender gap in AI is widening – not shrinking.

Nesta, the UK innovation agency for social good, conducted the study an evaluation from 2019 Das concluded that the proportion of AI scientific papers co-authored by not less than one woman has not improved because the Nineteen Nineties. In 2019, only 13.8% of AI research papers on Arxiv.org, an archive of pre-print academic papers, were authored or co-authored by women, with the number steadily declining over the previous decade.

Reasons for the inequality

The reasons for the inequality are diverse. But a Deloitte survey of ladies in AI highlights a number of the more outstanding (and obvious) elements, including the judgment of male colleagues and discrimination for not fitting into the established male-dominated molds of AI.

It starts in college: 78% of ladies who took part within the Deloitte survey said they didn't have a probability to get an internship in AI or machine learning while in college. More than half (58%) said that they had left not less than one employer on account of disparate treatment of men and ladies, while 73% considered leaving the tech industry entirely on account of unequal pay and the lack to advance of their careers.

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

Nesta's evaluation found that girls are more likely to think about social, ethical and political implications of their work in AI than men – which isn’t surprising on condition that women live in a world where they’re divided based on their gender and products The market was designed for men and ladies with children. They are sometimes expected to balance their work with their role as primary caregivers.

With any luck, TechCrunch's humble contribution – a series about achieved women in AI – will help move the needle in the proper direction. But there’s clearly still quite a bit to do.

The women we profile share many suggestions for many who need to advance and advance the AI ​​field for the higher. But a standard thread runs through: strong support, commitment and role model function. Organizations can create change by adopting policies—whether in hiring, training, or otherwise—that promote women who’re already within the AI ​​industry or need to enter the AI ​​industry. And decision-makers in positions of power can use that power to advocate for more diverse, supportive workplaces for ladies.

Changes won't occur overnight. But every revolution begins with a small step.


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