The Leading Women Shaping the Future of AI: 2020‘s Top Female Influencers

ByJordan Brown

The rapid rise of artificial intelligence (AI) and machine learning (ML) is transforming industries and impacting almost every facet of our lives. As these technologies grow more sophisticated and ubiquitous, it‘s critical to ensure that the people building and shaping them represent our diverse human perspectives and experiences.

While women have historically been underrepresented in computing fields, a growing number of female leaders are emerging as influential voices in the AI/ML community. In 2020, these women made significant contributions to advancing the state-of-the-art in AI/ML while also advocating for the ethical and responsible development of these powerful technologies.

In this article, we‘ll dive deep into the work and impact of seven of the top female AI influencers of 2020. These women are pioneering novel machine learning techniques, building impactful AI-powered applications, creating tools and datasets used by thousands of researchers, and advocating for greater diversity and ethics in the field.

Fei-Fei Li: Computer Vision Pioneer

Fei-Fei Li is a professor of computer science at Stanford and co-director of the Stanford Institute for Human-Centered AI (HAI). She is a pioneer in computer vision, a branch of AI focused on enabling machines to interpret and understand visual information from the world.

Li‘s most notable contribution is ImageNet, a publicly available dataset containing over 14 million annotated images across 22,000 categories. ImageNet has been a key enabler of the deep learning revolution in computer vision over the last decade. To date, the dataset has been cited in over 40,000 research papers.

In addition to her research, Li is a prominent advocate for "human-centered AI" that enhances and empowers people rather than replacing them. Through her nonprofit AI4ALL, she works to increase diversity and inclusion in AI by offering education programs to underrepresented groups. Over 500 students have graduated from AI4ALL programs at top universities.

Daphne Koller: Accelerating Drug Discovery with AI

Daphne Koller is the founder and CEO of Insitro, a startup using machine learning to accelerate drug discovery. Previously, she was a professor of computer science at Stanford and co-founder of the online education platform Coursera, which has reached over 20 million learners.

At Insitro, Koller and her team are pioneering "in silico" drug discovery. They use large biological datasets to build predictive ML models that identify promising drug targets and candidates much faster than traditional approaches. Insitro has raised over $240 million in funding and partnered with leading pharmaceutical companies.

Koller is an expert in computational biology and has published over 200 research papers. In 2019, she was elected to the National Academy of Engineering, one of the highest honors in the field. She is passionate about using AI/ML to tackle high-impact challenges in healthcare.

Daniela Rus: Robot Innovator

Daniela Rus is the director of MIT‘s Computer Science and Artificial Intelligence Laboratory (CSAIL), one of the world‘s leading AI research centers. She is a pioneer in robotics, a field at the intersection of AI, computer vision, and mechanical engineering.

Over her career, Rus has developed many novel robotic systems that interact with and learn from the physical world. Her lab at CSAIL has produced robots for a wide range of applications, from manufacturing to search-and-rescue to medical surgery.

Some of her most notable work includes:

  • Soft robotic grippers that use specialized materials and AI control systems to handle delicate objects
  • Algorithms for multi-robot coordination that enable swarms of simple robots to collectively tackle complex tasks
  • Underwater robots that autonomously monitor marine environments and infrastructures

Rus frequently speaks out about the importance of diversity in driving innovation. 40% of CSAIL faculty are women, twice the national average for computing programs. The lab runs many outreach programs to inspire K-12 students, especially girls, to pursue computing and AI.

Joy Buolamwini: Algorithmic Bias Fighter

Joy Buolamwini is the founder of the Algorithmic Justice League (AJL), an organization that combines art and research to raise awareness about bias in AI systems. She is known for her groundbreaking studies uncovering race and gender disparities in commercial facial recognition systems.

In 2018, Buolamwini published "Gender Shades," a study showing that facial analysis systems from top tech companies had significantly higher error rates for darker-skinned and female faces. All companies improved their accuracy after her findings were released. Her follow-up project "AI, Ain‘t I a Woman?" found similar biases in AI systems used by leading companies and government agencies.

Buolamwini‘s work has been instrumental in the movement for algorithmic fairness, transparency and accountability. In 2020, her research and activism contributed to companies like IBM, Amazon and Microsoft announcing moratoria on selling facial recognition to law enforcement due to risks of misuse and discrimination.

Timnit Gebru: Ethical AI Advocate

Timnit Gebru is the founder of the Distributed AI Research Institute (DAIR), an independent lab focused on ethical AI research and advocacy. Previously, she co-led Google‘s Ethical AI team before her high-profile exit in late 2020 after raising concerns about censorship.

Gebru‘s research focuses on uncovering hidden biases and negative consequences of large-scale AI systems. In 2020, she co-authored a groundbreaking paper, "On the Dangers of Stochastic Parrots," which highlighted the significant environmental and societal costs of popular large language models and called for more responsible AI development practices.

Gebru has been an outspoken advocate for diversity in tech and more ethical and inclusive AI development. She co-founded the affinity group Black in AI, which now has over 2,000 members, and co-organized the Fairness, Accountability and Transparency conference.

Since leaving Google, Gebru has used her platform to raise critical questions about power imbalances between tech giants and AI ethics researchers attempting to hold them accountable. Her ongoing advocacy is pushing the field towards greater transparency and consideration of societal impacts.

Dina Machuve: Applying AI for African Development

Dina Machuve is a lecturer and researcher leading the AI and Data Science research group at the Nelson Mandela African Institution of Science and Technology (NM-AIST) in Tanzania. Her work focuses on applying AI and data science to tackle development challenges across Africa.

Machuve‘s projects span a range of areas including:

  • Using machine learning on satellite imagery to predict crop yields and food insecurity
  • Developing AI diagnostic systems to detect diseases like malaria from medical images
  • Applying data mining to inform education policy and personalize learning

She is passionate about building local capacity in AI and data science across Africa. Machuve has spearheaded programs like Data Science Africa and the ZindiWeekendz learning series on the data science competition platform Zindi, which have provided training to hundreds of African data scientists.

Machuve also works to combat "data colonialism" and ensure Africans have control over their data and a seat at the table in shaping AI development. She emphasizes engaging local expertise and context and creating AI solutions "in Africa, for Africa, by Africans."

Rana el Kaliouby: Pioneering Emotion AI

Rana el Kaliouby is the co-founder and CEO of Affectiva, a startup using AI to analyze human emotions from facial and vocal expressions. She invented the company‘s patented emotion recognition technology during her research at MIT.

Affectiva‘s technology is used by over 1,400 brands to gain insight into consumer emotional responses. The company has also developed an "Emotion AI for Gaming" platform to create more immersive and personalized gaming experiences. In 2020, Affectiva expanded into automotive AI, building systems to track driver attention and drowsiness.

As a Muslim woman in AI, el Kaliouby has been a prominent advocate for diversity. Less than 14% of AI researchers globally are women, and Middle Eastern women like her are significantly underrepresented. Through her leadership and advocacy, el Kaliouby aims to inspire more women and minorities to enter the field.

El Kaliouby is also passionate about the ethical development of emotion AI. While the technology has potential for good, such as improving mental health monitoring or personalizing online learning, it also raises critical privacy concerns. She has called for industry standards and guardrails to prevent misuse.

These seven women are just a few examples of the incredible female leaders shaping the AI field. Through their technical contributions, advocacy, and mentorship, they are paving the way towards a future of AI that is more diverse, ethical, and equitable.

However, significant work remains to make the AI community more inclusive. A 2020 report from the AI Now Institute found that only 18% of authors at leading AI conferences are women, and over 80% of AI professors are men. Women of color face even greater obstacles to advancement.

As AI grows more widespread and influential, it‘s imperative that the people building these systems reflect the full depth and breadth of human diversity. The perspectives and lived experiences these women bring are invaluable in ensuring AI works for everyone. Supporting and elevating diverse voices will enable us to create AI systems that are more fair, accountable and beneficial to society.

Looking ahead, the women profiled here will undoubtedly continue to be at the forefront of innovation in AI. From fundamental research to real-world applications to advocacy for ethical development, they are shaping the trajectory of one of the most transformative technologies of our time. As aspiring AI practitioners and leaders, we should all seek to learn from and build upon their important work.

To dive deeper into these influencers, here are some additional resources:

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