Women in AI: Creating community with IBM’s Ritika Gunnar

Presented by IBM
Photo Ritikia Gunnar IBM

Diversity undoubtedly benefits the creative process, helps solve business problems and results in better products, yet when it comes to Artificial Intelligence (AI), women continue to represent only 10 to 15% of the workforce.

IBM’s Women Leaders in AI is looking to change this statistic through mentorship, community-building and showcasing the incredible technical leaders transforming the field of machine learning. We had the chance to speak with Ritika Gunnar, VP Data & AI Expert Services & Learning at IBM, about ways of growing more diverse teams in this space.

Q: Can you tell us about the findings in IBM’s study of women in AI?

A: We interviewed 2,300 organizations to shed light on the challenges of being a woman in AI. Only 27 percent of survey respondents say achieving gender equality is a challenge for their organization. But the most common hurdles are facing the younger generation… Younger women were much more likely to be told that they have a natural talent for something other than math and sciences. One of the things I think about is why get into AI and the sciences? It’s very important to do it early. You think reading, writing and arithmetic are the three pillars that you learn when you go to school but I always talk about reading, writing, arithmetic and Python! You have to immerse yourself young so you can feel comfortable with the technology and to see yourself in it. Having support and examples of women leaders in AI is really important for young women.

 

Fast facts from the IBM study

● Women hold 18% of senior leadership positions among 2,300 organizations surveyed.
● Men occupy 82% of the most influential roles in these organizations.
● Promotion of women is not a business priority for 79% of surveyed organizations.

 

Q: Do you have any ideas about how to get more women into the field?

A: I have two children and one of them is a young daughter, so a lot of my understanding came from watching her with technology. There’s a lot of material out there on how to get started with artificial intelligence, how to start coding and doing Python, but the thing that you need is to surround yourself with the right support group. When my daughter first started, she came home and told me, ‘I don’t want to code anymore, Mom.’ And I was like, ‘Why not?’ I realized she was in a coding class with all boys, so she didn’t have community, support or a way to relate. So I sent her to a class that had a little bit more diversity, where she could grow with her peers. Now, I can send her to any class and she has the confidence to do it on her own.

Whether you’re an 11-year-old like my daughter, an adult like myself or someone out of college, it’s important to find a community where you can prepare, propel yourself to continually learn and stretch in ways where you feel comfortable making the mistakes that you need to. That support system is probably one of the most important things… You’ve got to be in a community where you feel comfortable enough to ask questions, learn and understand that when you fail, you can do it again. It is all about creating a culture where you can learn from each other.

 

Read more

IBM honours women who are putting AI to work in business: view the list.

 

Q: What other insights about diversity in machine learning can you share?

A: Anytime you have diverse perspectives and backgrounds, you open yourself up for a better opportunity to think about alternative approaches to problems and solve challenges differently… it allows for new ideas to help accelerate progress not just with the technology, but the outcomes that the technology delivers. As with anything, when you lack diversity, you become very narrow in how you can approach problem solving and attacking things because you think about it only from the ways in which you know, in what is traditional.

Q: What skills should hiring managers look for in this space?

A: Having diversity brought forward, not just by men and women but by the diversity of backgrounds, is key. When we look for people who are joining data science, we look for people who have this culture of curiosity and continuous learning. This knowledge is not directly related to males or females, but the average lifespan of skills – and the market is three to five years in the space of AI; it’s a lot smaller. So we look for people who do not necessarily come from a technology background [but] who have backgrounds that are varying in nature, who have been able to solve complex problems and show that they have that hunger for continuous learning, that’s the most important characteristic. And if you can augment that with emotional intelligence, with really understanding not only the technology but the people and the process pieces, you can go a long way. Those two things are so important to take AI and have it be outcome driven, and then scale it across an organization.

This interview has been edited and condensed for length.

 

Want more?

Ritika will be speaking about how neuro-linguistic programming is transforming industries at the IBM Think Digital Event Experience May 5-6. #Think2020

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