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To give AI-focused<\/span> women academics and others their well-deserved \u2014 and overdue \u2014 time in the spotlight, TechCrunch is launching a series of interviews<\/a> focusing on remarkable women who\u2019ve contributed to the AI revolution. We\u2019ll publish several pieces throughout the year as the AI boom continues, highlighting key work that often goes unrecognized. Read more profiles here<\/a>.<\/p>\n As a reader, if you see a name we\u2019ve missed and feel should be on the list, please email us and we\u2019ll seek to add them. Here are some key people you should know:<\/p>\n In a New York Times piece<\/a> late last year, the Gray Lady broke down how the current boom in AI came to be \u2014 highlighting many of the usual suspects like Sam Altman, Elon Musk and Larry Page. The journalism went viral \u2014 not for what was reported, but instead for what it failed to mention: women.<\/p>\n The Times\u2019 list featured 12 men \u2014 most of them leaders of AI or tech companies. Many had no training or education, formal or otherwise, in AI.<\/p>\n Contrary to the Times\u2019 suggestion, the AI craze didn\u2019t start with Musk sitting adjacent to Page at a mansion in the Bay. It began long before that, with academics, regulators, ethicists and hobbyists working tirelessly in relative obscurity to build the foundations for the AI and GenAI systems we have today.<\/p>\n Elaine Rich, a retired computer scientist formerly at the University of Texas at Austin, published one of the first textbooks on AI in 1983, and later went on to become 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<\/a> and distributed computing. And Cynthia Breazeal, a roboticist and professor at MIT and the co-founder of Jibo<\/a>, the robotics startup, worked to develop one of the earliest \u201csocial robots,\u201d Kismet, in the late \u201990s and early 2000s.<\/p>\n Despite the many ways in which women have advanced AI tech, they make up a tiny sliver of the global AI workforce. According to a 2021 Stanford study<\/a>, just 16% of tenure-track faculty focused on AI are women. In a separate study<\/a> released the same year by the World Economic Forum, the co-authors find that women only hold 26% of analytics-related and AI positions.<\/p>\n In worse news, the gender gap in AI is widening \u2014 not narrowing.<\/p>\n Nesta, the U.K.\u2019s innovation agency for social good, conducted a 2019 analysis<\/a> that concluded that the proportion of AI academic papers co-authored by at least one woman hadn\u2019t improved since the 1990s. As of 2019, just 13.8% of the AI research papers on Arxiv.org, a repository for preprint scientific papers, were authored or co-authored by women, with the numbers steadily decreasing over the preceding decade.<\/p>\nThe gender gap in AI<\/h2>\n
Reasons for disparity<\/h2>\n