As companies continue to fill more than a million surplus jobs, one of the top roles they’re hiring for is artificial intelligence specialist. It’s great to see a strong focus on AI and machine learning as they become more prevalent across industries. However, these companies may not be searching as far and wide as they could be.
Per a World Economic Forum-LinkedIn study, women make up only 22% and 12% of global AI and machine learning professionals, respectively. What’s creating this gap? The answer, of course, is complicated. Is it an unwillingness to interview women for these types of roles? Is it a lack of opportunity for women to move up within the organization? Maybe job postings aren’t being written in appealing ways?
All those factors (and more) are at play. But one thing remains true: this gap limits the effectiveness of companies. Instead of having diverse and varied hires, there’s a danger of too much similar thinking within the organization. This can put a limit on what it can accomplish.
Dreaming Big to Bring More Women into AI
One of the coolest programs at DataRobot is Dream Big. Dream Big guides employees to determine and define their long-term career and personal goals, then puts employees on the right track to accomplish them.
A part of the program that I really enjoyed was this thought exercise; we were asked to develop our Dream Big Legacy Goal — something we wanted to accomplish several years in the future. Then, we worked backward to figure out how we could reach that goal, moving in smaller increments.
This exercise was a challenge I hadn’t fully considered before. It had to define my steps, ask for help and feedback and focus on what I wanted to achieve. In this case, I wanted to bring to life the Women in AI Camp (WaiCAMP), an introductory virtual course on artificial intelligence and applied data science — entirely in Spanish. The AI gender gap is especially large in Latin America, and we see this camp as a way to contribute to the inclusion of women in AI roles, projects and initiatives in a positive way.
Considering the thought exercise, some of my motivating factors are to contribute to the economic development of Latin America by accelerating the adoption of AI and to reduce the gender gap in AI. Those motivations require a lofty goal and breaking it down has been helpful in maintaining a solid path.
My Dream Big Legacy
My Dream Big Legacy goal is that in five years, I will have mentored and trained 1,000 women in AI in Latin America through the WaiCAMP by DataRobot University; my one-year goal was to launch the camp; my six-month goal was to reach out to women and secure enough interest to have 60 applicants.
By breaking the overarching goal down into smaller achievements, I’m more likely to accomplish each of them. However, I still get to celebrate impactful wins along the way, and in this case, I’m doing so in the presence of some incredible women!
Through these smaller achievements, I also learn what works and what doesn’t and can course correct to help reach future goals. And I’m not alone either. By having the fundamental ability to understand how to apply AI and machine learning in certain areas, other women can launch impressive projects to build on that foundation.
For example, neuroscientist and technologist Poppy Crum coined the term “hearables” and works with Dolby to develop neuroscience algorithms. Her work will continually and silently assess and anticipate people’s needs and state of mind — it’s like a USB outlet for the ear.
Or how about Dr. Radhika Dirks of XLabs? She’s developing AI-enabled strategies to improve upon social distancing and creating projects focused on accelerating vaccine and antiviral discovery for COVID-19. We may never return to the “before times” of pre-COVID life, so this work is beneficial for our well-being.
Additionally, this foundational knowledge plays an important role in reskilling and upskilling as new technologies emerge. Twenty years ago, many people didn’t fully understand AI or machine learning, and these won’t be the last technological advancements we see.
How Can We Start Closing This Gap?
The first step toward closing this gap is acknowledging it exists. Company and industry leaders must recognize this is an issue, but they can’t stop there. They must take action, too.
Here are some places to begin:
- Continue (or start) making company culture gender-inclusive. Culture builds the foundation for employee productivity and other company initiatives. If women feel like they aren’t being heard or recognized for their efforts, you may see their productivity start to slip. A gender-inclusive culture extends to hiring efforts as well. Cast a wide net during new hire searches. Don’t immediately reject someone with a “unique” or less common background and consider AI tools to ensure your job postings aren’t using discouraging language.
- Dedicate company resources to training and educating. Whether you’re bringing in outside speakers or teachers to present information or allowing women to pursue additional degrees or certifications, an environment of continuing education can lead to unparalleled success.
- Introduce opportunities to contribute to AI projects within the company. Far too often, there’s a small circle of trust when it comes to AI, which often leads to unconscious biases, opening up potential pitfalls. Allow women of all levels to participate to bring in diverse perspectives.
- Highlight women trailblazers in the AI space. It’s hard to know about some of the career paths or opportunities available if you’ve never seen anyone else accomplish them. Initiatives like Women’s History Month shine a light on women doing wonderful things, but we can continue those celebrations throughout the entire year. Bring in outside speakers, encourage the reading of women authors within AI, and look for other ways to amplify the good work of women inside and outside your organization.
Leaders must also realize the benefits of having women in AI and leadership positions. Companies with at least one diverse hire — with a focus on women — see a 44% increase in their average share price within a year of going public, compared to 13% that don’t have a diverse hire.
Additionally, having more diverse voices in AI can introduce new ideas, career paths, products, and services. This space is still so new that we’re able to shape it in exciting ways, though it won’t happen if we don’t create an inclusive environment.
As long as AI and machine learning lack diverse perspectives, they will produce biased results. It may not even be obvious, but too much of the same thinking will miss the same blind spots, limiting the effectiveness of AI models.
From an educational perspective, teachers, mentors, and even journalists can highlight how regularly we use AI, data science, and STEM topics and present them in ways that are approachable. The following organizations are doing their part in that regard:
- Women in AI combines education with research and networking and highlights why having a support system is so critical. I worked with Women in AI – Mexico in building and distributing the WaiCAMP by DataRobot University program. The Women in AI network of more than 5,000 members has strong relationships with tech companies like AWS, Microsoft, and IBM and their experience with community, events, education, and logisitics make them a perfect partner!
- GeekPack teaches the importance of gaining skills while building a community through coding. No one ever needs to go it alone on their AI, STEM, or data science journey.
- HipHopEd introduces STEM subjects through a hip-hop lens to build upon students’ existing interests. For example, it showcases the need for engineering skills to enter into a field like designing shoes. Most students would never put those two disciplines together!
- QA’s Teach the Nation to Code delivers talent and training services to help individuals and companies thrive in the digital revolution.
To be most effective, this education needs to start early—and it must be consistent.
Understanding AI in Daily Life
Before any of those advancements occur, though, we must take a step back and realize AI is penetrating our lives, sometimes in ways we don’t even recognize. From a citizen data scientist perspective, the more you understand how AI, machine learning, and algorithms work, the better it will help in your daily lives.
Chatbots such as Capital One’s Eno automate basic queries while also providing personalized recommendations and goals. These chatbots help reduce wait times (and frustration levels) of customers looking for financial information.
AI-powered mortgage advisors use machine learning to determine whether a customer will qualify for a mortgage, suggest proper tools and products, and give tips for boosting credit scores. Buying a home is a big commitment and takes a lot of research, so these tools help expedite that process.
Even the DMV — one of the most notoriously slow services we deal with — has used AI-backed tools like edge-based customer forms and interactive voice response to avoid those long lines and make our lives easier.
The above examples are just a few of the ways AI is becoming more ingrained into our lives, and we’re still only scratching the surface of what’s possible. Yet those possibilities will be limited unless we get more diversity into AI. The all-encompassing AI landscape makes it critical to have more women in the field, avoiding unconscious bias and creating more well-rounded and comprehensive AI models.
When everyone is able to meaningfully contribute to our industry, success is far more likely. I encourage you to be curious about AI — you just might be surprised where it can take you.