Why Women Are Making It Big in Artificial Intelligence and Machine Learning

Women are making meaningful headway in the field of machine learning and artificial intelligence. Read on to learn more about the challenges women are overcoming and the opportunities that are helping close the gender gap.

Women in Machine Learning

It’s no secret that STEM professions—shaped by years of gender and racial bias—lack diversity. Machine learning engineering and research is no exception. Women currently hold around 25% of all computer science-related jobs, and only 12% of machine learning roles, with factors such as a lack of pay and career advancement transparency and a lack of women role models contributing to those numbers.

But leaders in the machine learning and AI industry have in recent years woken to the value that women bring to the workforce. It doesn’t just look good for a company to have diversity—it’s integral to the success of organizations that build machine learning algorithms and artificial intelligence. Having more women working on machine learning and AI can alleviate the issue of selection bias, which is one of the biggest problems facing AI enterprises. “In order for organizations to achieve the highest AI maturity levels, it is necessary to mobilize women on a mass scale and include them as part of all enterprise endeavors in artificial intelligence, from research to product launch,” according to a report from Forbes.

To that end, tech execs have made it their mission to diversify their hires and teams, overhaul toxic cultures, and prevent attrition. Many women machine learning engineers and researchers have also taken matters into their own hands, creating opportunities and support networks for women who are either considering or have already embarked on their technology careers, supporting young women through mentorships, and clearing some of the hurdles that stand in the way of women who have a technical background.

How Can We Get More Women in Artificial Intelligence and Machine Learning Careers?

Many of the challenges that stand in the way of women building long and satisfying careers in machine learning engineering and research are systemic and have proven difficult for organizations to dismantle. The good news is that many companies, industry leaders, and women have stepped up to the challenge—the issue of diversity is being prioritized in professional conversations, organizations are attempting to build a more supportive environment, and women are finding ways to exchange ideas and share information in ways that help each other advance their careers. 

  • More women role models. Studies have shown that women role models can empower and inspire women to stay in the workforce and pursue more advanced roles. In one particular study, researchers found that female students were more likely to major in STEM if they were assigned a woman professor, and that junior-level employees were more likely to stay with an organization if they had women supervisors. Companies are beginning to understand the importance of not only having women machine learning engineers, software engineers, statisticians, and data scientists, but also having women at all levels of an organization so that their peers can see both a path for advancement and a place at the top for them.
  • Pay and promotion transparency. The gender pay gap in tech can range from 5-10%, according to Hired, and the frustration of not knowing where they stand or how much they should be asking for is one of the reasons women in machine learning feel disempowered. More transparent conversations about salaries can help break down these barriers of disempowerment, according to Hired’s report, which also found that 68% of women and 63% of men surveyed thought that pay transparency would increase their interest in working for a company.
  • Parental leave. Parental leave and support for new parents reentering the workforce plays a huge role in preventing attrition. A study by the Harvard Business Review found that 41% of women in tech roles will leave the industry after their tenth year on the job, and that many women entering their thirties often plan their exit. Many factors contribute to the churn, from sexist workplaces to pay discrepancies, but the lack of support for parents is an oft-cited factor.
    Many big tech companies have begun addressing this by offering months-long maternity and paternity leave policies for new parents, providing adoption and surrogate support, and covering fertility treatments and egg freezing. Some companies, like Amazon, have also launched programs that allow new parents to gradually increase their workload when they return, or to work flexible, remote hours. 
  • Understanding the value women bring to the field. Machine learning algorithms and AI take on their creators’ biases and prejudices, which is a significant problem when AI is used in high-stakes contexts such as law enforcement and immigration. Racial and gender biases in AI are such a big problem that they can pose an existential threat to organizations who build and sell machine learning products. Which is why recognizing the importance of diversity in machine learning and AI development is a no-brainer for organizations. “Vision and strategy need to include diversity,” according to the World Economic Forum. “Non-homogenous teams are more capable than homogenous teams of recognizing their biases and solving issues when interpreting data, testing solutions, or making decisions.”

What Opportunities Are Available for Women in Machine Learning?

Every facet of machine learning, whether it’s AI, deep learning, robotics, machine learning research, statistics, or text mining benefits from diversity at all levels. Recognizing some of the barriers to entry that can deter women from careers in the profession or discourage them from climbing the ranks, machine learning engineer leaders, women leaders in AI, and prominent researchers from the machine learning community have launched conferences and initiatives to support women from the time they’re in school through to when they’re in the workforce.

Some of the more notable conferences, organizations, meetups, and networking opportunities include:

  1. Women In AI
  2. Women Leading In AI
  3. Women In Machine Learning
  4. Women In Analytics Conference
  5. Women In AI Global Summit
  6. NEURIPS
  7. The Rising: Women In AI And Analytics Conference
  8. WIMLDS

Scholarships and Grants for Women in Artificial Intelligence

A growing number of organizations have thrown their support behind helping tech companies achieve gender diversity, namely through offering scholarships, fellowships, grants, and outreach programs. Machine Learning scholarships are available for undergraduate and graduate students who are pursuing machine learning and artificial intelligence, computer science, natural language processing, or related fields, and there are also internship programs and hackathons that aim to give students from historically underrepresented groups hands-on industry experience.

On the online courses/bootcamp front, Springboard has partnered with Women Who Code to offer ten scholarships worth $1,000 each to women who enroll in Springboard’s Data Science Career Track, Software Engineering Career Track, or the Machine Learning Career Track.

Springboard also offers a number of women-in-tech scholarships

How Does Springboard Support Women Entering Artificial Intelligence?

Many graduates of the program credit Springboard’s three-pronged approach to their success:

  1. Ensuring students learn foundational software engineering skills
  2. Giving every student hands-on experience with real-world projects
  3. Comprehensive mentorship.

“I was learning something new every day,” said Diana Xie, a Springboard graduate from the Machine Learning Career Track who now works as a machine learning engineer at IQVIA.

“The self-paced structure can be stressful, and that was where interacting with my mentor and scheduling calls with Springboard coaches/advisors was helpful. It definitely challenged me and made me more comfortable, not just casually self-learning with the help of others, but taking it a serious step further to enter another career.”

Women Leading in Artificial Intelligence

Women Leading the Way in Artificial Intelligence

Despite the poor diversity numbers in the field of machine learning engineering, women occupy some of the most influential and pioneering roles in the profession. A few industry leaders include:

  • Anima Anandkumar — Director of research at NVIDIA
  • Kate Crawford — co-founder of AI Now Institute at NYU
  • Sanja Fidler — Director of AI at NVIDIA
  • Daniela Rus — Director of the CS and AI Laboratory at MIT
  • Claudia Pohlink — Head of Artificial Intelligence at Deutsche Telekom
  • Hye-young Kim — Director of Artificial intelligence at Lotte Shopping
  • Ivana Bartoletti — Technical Director at Deloitte
  • Kelly Combs — Director of Emerging Technology Risk at KPMG LLP, USA
  • Jennifer Edgin — CTO, Deputy Commandant Information in the U.S. Marine Corps

“Five most important things that I have learned and that are most important to me are the following,” said Ivana Bartoletti, technical director at Deloitte. 

  1. Having a mentor. Identify women you want to be in five years’ time and go and talk to them. Ask them to be a sounding board from time to time, and keep them posted on your progress.
  2. Be yourself, all the time. No point trying to change, and being yourself is much more fun, too.
  3. Know your stuff, but avoid comparing with others.
  4. Do not give an apology if you are convinced that you have done nothing wrong. This is so important to me.
  5. Work on your body language. Finally, and that is maybe because of my Italian upbringing, do work on your body language. Hold poses of confidence, use specific posture, gestures, and even clothing that make you feel confident.

Check out our other great content honoring women working in tech. Springboard is a proud partner of Women Who Code in addition to offering several scholarships for women in tech.

Is machine learning engineering the right career for you?

Knowing machine learning and deep learning concepts is important—but not enough to get you hired. According to hiring managers, most job seekers lack the engineering skills to perform the job. This is why more than 50% of Springboard's Machine Learning Career Track curriculum is focused on production engineering skills. In this course, you'll design a machine learning/deep learning system, build a prototype, and deploy a running application that can be accessed via API or web service. No other bootcamp does this.

Our machine learning training will teach you linear and logistical regression, anomaly detection, cleaning, and transforming data. We’ll also teach you the most in-demand ML models and algorithms you’ll need to know to succeed. For each model, you will learn how it works conceptually first, then the applied mathematics necessary to implement it, and finally learn to test and train them.

Find out if you're eligible for Springboard's Machine Learning Career Track.

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