Highlighting the Influential Women Making a Difference in AI
To provide much-deserved recognition to women academics and other specialists in the field of Artificial Intelligence, TechCrunch has begun a series of interviews celebrating exceptional women who have made significant contributions to the AI sector. In the progressing year as the AI industry surges, we aim to spotlight the imperative work that often goes unnoticed. Additional profiles can be found here.
If as a reader, you come across a notable name that we have missed and think should be included, please reach out to us via email and we’ll aim to include them. Here are a few key individuals you should be aware of:
In a piece published by The New York Times last year, the paper attempted to unpack the current influx in the field of AI — focusing mostly on the predictable candidates like Sam Altman, Elon Musk, and Larry Page. However, the report became a talking point not for what it discussed but for what it overlooked: women.
The list compiled by The Time’s included 12 men, a majority of them at the helm of AI or technology companies. Notably, many of these men lacking any substantive training or education in AI.
Contrary to what the Times suggests, the fascination with AI didn’t start with Musk conversing with Page at a mansion in the Bay. In fact, it began much earlier with scholars, regulators, ethicists, and enthusiasts diligently working in relative anonymity to lay the groundwork for the AI and GenAI systems we use today.
Elaine Rich, a retired computer science professor who used to teach at the University of Texas at Austin, published one of the first tAI textbooks in 1983. She later became the director of a corporate AI lab in 1988. Cynthia Dwork, a Harvard professor, made significant contributions decades ago in the AI fairness, differential privacy, and distributed computing domains. Additionally, Cynthia Breazeal, a roboticist, MIT professor, and the co-founder of Jibo, a robotics startup, developed one of the earliest “social robots,” Kismet, in the late ’90s and early 2000s.
Even though women have significantly contributed to AI technology, they are severely underrepresented in the global AI workforce. According to a 2021 Stanford study, women constitute a mere 16% of tenure-track faculty members focusing on AI. Similarly, findings from a separate study published the same year by the World Economic Forum, reveal that women hold only 26% of analytics-related and AI jobs.
Unfortunately, the gender gap in the AI field is growing wider as opposed to narrowing.
Nesta, which serves as the UK’s agency for fostering innovation aimed towards social benefit, carried out an analysis in 2019. The study found that the percentage of AI research papers having at least one female co-author did not increase since the 1990s. The data from 2019 reveals that a mere 13.8% of the AI research works on Arxiv.org (a site for sharing preliminary versions of scientific papers) were either written or co-written by women. Furthermore, there has been a consistent decline in these figures over the last ten years.
Several reasons contribute to this imbalance. However, a survey of women in AI conducted by Deloitte throws light on a few major and clear ones. These include criticism from male counterparts and bias due to the failure to fit into the existing male-dominated frameworks in AI.
The issue begins at the university level: 78% of the women who took part in the Deloitte survey revealed that they could not secure an internship in AI or Machine Learning during their undergraduate studies. Over half (58%) admitted to leaving a previous employer owing to differential treatment between men and women. Moreover, 73% considered quitting the tech industry entirely due to pay disparities and hurdles in career progression.
Such underrepresentation of women is detrimental to the field of AI.
Nesta’s study indicates that women are more prone than men to incorporate societal, ethical, and political aspects into their AI work. This is not surprising given that women face constant belittlement due to their gender, have to contend with products designed for men, and often have additional familial responsibilities as primary caregivers.
Our small contribution at TechCrunch, a series featuring accomplished women in AI, hopes to push for positive change. Nonetheless, there is still much work that needs to be done.
Among the many recommendations offered by the women we spotlight for the improvement and growth of AI, common elements include the importance of mentorship, commitment, and leadership. Instituting hiring and education policies that benefit and uplift women in the AI sector, or those who aspire to it, can bring about change at an organizational level. Individuals in power can also contribute by advocating for more diverse and woman-friendly workplaces.
Conflicts won’t resolve immediately. But remember, every significant change begins with a small step.
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