Women in AI: Breaking the Internet Glass Ceiling

When you think about it, a career in artificial intelligence might not seem like an obvious choice for women.

After all, the field is dominated by men. But that might not be the case for long. According to the Indeed AI job report from September 2021, the number of job openings for AI professionals has increased by 44% since 2016. With so many opportunities opening up, women are rushing to fill them.

Why the sudden interest in AI? There are several potential reasons. For starters, AI is here to stay. So, it makes sense that tech professionals and founders would want to be a part of it from day one. Secondly, there’s substantial job security in the field of Artificial Intelligence.

As AI becomes more and more pervasive in our everyday lives, it’s not hard to imagine what the future holds for professional AI professionals. Below is a shortlist of women in Artificial Intelligence who are breaking the glass ceiling and rising through the ranks. 

AI Founders

Artificial intelligence is an exciting field, but it’s also male-dominated. In fact, just four percent of AI researchers are women. But that’s not stopping them from making their mark on the world of artificial intelligence. Take, for example, Dr. Fei-Fei Li. She’s a professor at Stanford University and chief scientist at Google Cloud AI & ML Research. Dr. Li heads up a team of more than 100 scientists and engineers who are working to make artificial intelligence more accessible and understandable by anyone who wants to use it — including women!

Joy Buolamwini: Founder, Algorithmic Justice League

One woman leading the charge to get more girls interested in AI is the Founder of the Algorithmic Justice League, Joy Buolamwini. She’s a graduate student at MIT who became interested in AI because she wanted to find a way to stop biases from influencing facial recognition software.

Buolamwini wanted to test if the tools we use can detect our gender and race so that they can be corrected for future use. So, she submitted a photo of herself as part of an experiment with four other people – one white man, one Asian woman, one Latino man, and one African-American man – to see how various facial recognition systems would detect it.

The results were shocking. The system couldn’t tell Buolamwini was female and had classified her as Asian instead. That meant that her face was mislabeled or mismatched for over half (22 out of 36) of the 34 facial recognition tools on the market.

Daphne Koller: CEO & Founder, insitro

Daphne Koller is the CEO and founder of insitro. She originally studied neuroscience at Stanford University and then became a professor when she joined the faculty at Google in 2001.

In 2011, she founded insitro after noticing that there were no companies developing effective tools for drug discovery in the field of regenerative medicine. The company aims to solve this problem by creating better ways to find drugs for neglected diseases.

Koller is also committed to empowering more women in tech and entrepreneurship. In 2014, she launched her own incubator program called Imagine K12, which invests in high-quality education startups focusing on giving women opportunities to succeed.

Rana el Kaliouby: CEO & Co-Founder, Affectiva

One woman that’s helped bring this conversation to the forefront is Rana el Kaliouby. She’s the CEO and co-founder of Affectiva, an AI company specializing that specializes in emotion detection. The company has created a piece of software that can identify any individual’s emotions by analyzing their facial expressions. You may have seen it recently on YouTube when a Coca-ColaCoca Cola ad was released with an automated version of the software highlighting how long you watch the video before your emotions change.

El Kaliouby herself is a pioneer in AI and has been recognized as one of the industry’s most notable female leaders. She has spoken at numerous conferences, including TEDx, Intel Corporation, and MIT Media Lab, where she discussed her efforts to help people become more emotionally intelligent through technology.

Anna Patterson: Founder & Managing Partner, Gradient Ventures

Anna Patterson is a founder and managing partner of Gradient Ventures. She also served as the VP of engineering at Google for nine years.

Gradient Ventures is an AI-focused venture capital firm that invests in startups with female co-founders who are working on AI products or services. One reason why Patterson founded Gradient was that she felt like there were so many talented women in the field, but they weren’t getting the recognition they deserved.

Patterson’s goal with Gradient is to provide these female entrepreneurs and engineers with the resources, connections, and mentorship they need to build their own successful companies.

Other female AI founders include:

* Elizabeth Stark, co-founder of Lightning Labs

* Lucy Peng, president of DiDi Chuxing

* Marjorie Scardino, former CEO of Pearson Publishing

* Kanwal Rekhi, founder and chairman of SilkRoad Group

AI Leaders

Claire Delaunay: VP Engineering, NVIDIA

Claire Delaunay is VP of Engineering at NVIDIA. She’s been in the industry for over 20 years and has seen firsthand how AI has changed the way people work. As she puts it, “AI is starting to change the way we do everything.”

Delaunay is one of the women leading the charge for equality in AI. One of her most recent projects includes a course teaching people about data analytics and machine learning. More than 100,000 students have signed up so far.

“I really wanted to do something to get more women into this,” Delaunay said. “It’s not enough to just keep on complaining that there are not enough women doing this.”

She hopes that by educating more girls about technology, they’ll be able to see themselves as future innovators in the field.

Fei-Fei Li: Professor of Computer Science, Stanford University

One of the world’s leading experts on AI, Fei-Fei Li is a Professor of Computer Science at Stanford University. In 2012, she was named editor-in-chief of the Journal of Artificial Intelligence Research.

Though she no longer runs her own lab at Stanford, Fei-Fei Li remains an active researcher in the field of artificial intelligence. She’s also one of the first people to develop and teach a course on deep learning. Beyond that, she has written several books on artificial intelligence and machine learning.

Daniela Rus: Director, MIT’s Computer Science and Artificial Intelligence Lab (CSAIL)

One of the women in AI who is breaking the glass ceiling is Daniela Rus. She is the director of MIT’s Computer Science and Artificial Intelligence Lab (CSAIL). Rus’ lab focuses on deep learning, robotics, and more.

Rus has received many awards for her work in AI. In 2015, she was awarded the Turing Award – often referred to as the Nobel Prize in computing – which recognizes exceptional contributions to computer science.

In addition to being a pioneer in her field, Rus is also an advocate for female scientists. She was one of 500 women scientists who signed an open letter defending climate change research from U.S President Donald Trump’s EPA Secretary Scott Pruitt back in July 2017.

Shivon Zilis: Board Member, OpenAI; Project Director, Neuralink

AI is a booming industry.

Zilis was featured on Forbes 30 Under 30 list in 2016, and she’s been the recipient of numerous awards from various organizations. As the Board Member, OpenAI, and Project Director, Neuralink, she’s already impacted this field.

She has a bachelor’s degree in Computer Science from Brown University and a Master’s degree in Computer Science from Stanford University. Zilis is also one of the only female CEOs at a VC-backed Artificial Intelligence company.

Resources for women in AI

Women in AI

Women in AI was built to close the gender gap in the industry. Founders Moojan Asghari, Hanan Salam, and Caroline Lair’s goal are to empower women in AI with access to education, networking, and accessibility to join the field.

AI Now

AI Now’s mission is to analyze the social impact of AI tech and determine ways to analyze and improve the current ingest. Founded by Kate Crawford and Meredith Whitaker, AI now focuses on inclusion, bias, rights, and liberties. They hold annual symposiums at their NYU location.

Women in Machine Learning

Women in Machine Learning is a group focused on bringing more women into the AI industry. They help women find opportunities, grow professionally, and increase their impact in a work environment. The workshop happens yearly – and despite its name, it’s open to all genders.


AI4All focuses on improving diversity in the AI industry. AI4All partners with schools and universities to provide mentorship + recognition in the field of machine learning.


Acell.AI is focused on bringing much-needed diversity into the industry. Their founder, Laura Montoya, also founded the Latinx in AI Coalition.


Fast.AI is designed to make learning more accessible with software, applications, and free courses. This community is especially valuable for those looking to venture into the AI industry.

Women in Machine Learning & Data Science

Women in Machine Learning & Data Science hosts workshops, networking events, and hackathons to support and promote women in tech. 

Women in AI Podcast

“This podcast series will bring together a cross-disciplinary mix of influential women working in AI, Deep Learning, and Machine Learning and their impact on solving challenges in business and society.”

Talking Machines Podcast

Talking Machines is your window into the world of machine learning. Your hosts, Katherine Gorman and Neil Lawrence bring you clear conversations with experts in the field, insightful discussions of industry news, and useful answers to your questions. Machine learning is changing the questions we can ask of the world around us. This podcast explores how to ask the best questions and what to do with the answers.

What are we really teaching AI? TEDTalk playlist

A glimpse inside what we’re teaching artificially intelligent machines and a cautionary tale of what could happen if we get it wrong.

Talks by brilliant women in STEM TEDTalk playlist

These women are trailblazers inspiring a new generation of girls to follow their lead and change the ratio in STEM fields (science, technology, engineering, and math).

Why Gender Diversity in AI Matters

The first and most obvious reason to diversify the AI community is to increase the contribution to and speed at which solutions can be discovered. Diversity within organizations invites diversity of thought and increases bottom lines. Women only hold 29% of the jobs in professional AI. In order to optimize success, it’s essential to invite more voices representing unique experiences to the table. The varying viewpoints and perspectives allow for a wider lens with which to problem solve. 

Computer engineers and machine learning experts build AI systems designed to simulate our human ability to process information + take action upon it. What happens when an AI is built by just one mind? The tech will be limited—increasing diversity in the workplace, whether gender or race, simply results in better products.

But there are other reasons why gender diversity matters in AI, too. For starters, when more diverse teams work together on projects, they achieve better results. It’s like if all the players on a basketball team could sink three-pointers, but could not land a layup, and some team members didn’t know how to play defense. You would never win a game with that team composition, no matter how talented they were! In fact, studies show that diverse groups make better decisions than homogenous groups 90% of the time.

In short, gender diversity matters because it leads to better outcomes in the workplace. There’s no excuse not to be inclusive when so many benefits can come from it!


What is AI?

Artificial intelligence is the simulation of human intelligence processes by machines, especially computer systems.

What are the benefits of working in AI?

There are many benefits to working as an AI professional. For starters, you’ll be able to work remotely, giving you more flexibility. You’ll also be able to explore your creativity and follow your own interests in this field.

Is there room for women in AI?

Yes! According to Indeed’s September report on AI job openings, women make up 20% of applicants for these jobs. That’s a significant increase from just 10% in 2016.

Should I consider AI as a career?

If you’re interested in machine learning, data, and computer science and want to get involved with it early on, then this could be the perfect career choice for you.

What percentage of AI is female?

Women are typically underrepresented in AI, but this is beginning to change.

According to the Indeed AI job report, women make up 39% of all AI professionals. This percentage has been steadily increasing over time. The Indeed AI job report from September shows that the number of jobs for female AI professionals increased by 56% since 2016.

That being said, there’s still a long way to go before it reaches an even split between men and women.

Why do we need more women in AI?

Simply put: Diversity leads to new ideas and better solutions.

It’s no secret that the AI industry is dominated by men. In fact, it was just last year that Google published research showing that only 17% of those with professional-level AI skills are women. But there are a number of reasons why this disparity needs to be eliminated.

Secondly, as AI becomes more pervasive in our day-to-day lives, it will become more and more critical for professionals to understand how this technology can impact their business and industry. If you want your company to stay ahead of the curve (and survive), you need qualified people who can help develop AI products and services for your company.

What are the four types of AI?

There are four types of AI:

  • Machine Learning: This is where the computer can learn from data and make predictions based on experience. Machine learning can be used for fraud detection, customer service, text document categorization, and reading medical scans.
  • Natural Language Processing: The computer analyzes written or spoken language in order to interpret it. It can be used for voice recognition software and chatbots that can answer questions about a company’s services.
  • Computer Vision: This type of AI is used by autonomous cars to interpret the environment around them so they can make decisions accordingly.
  • Robotics: Robots are perfect for tasks that are either too dangerous or difficult for humans to do, like bomb disposal or entering dangerous environments without contaminating them with human contact.

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