stub Ritika Gunnar, VP of Expert Labs, IBM Cloud and Cognitive Software - Interview Series - Unite.AI
Connect with us


Ritika Gunnar, VP of Expert Labs, IBM Cloud and Cognitive Software – Interview Series




Ritika Gunnar is the Vice President of Expert Labs, IBM Cloud and Cognitive Software. She is also very involved in IBM's Women Leaders in AI program. Previously, she managed IBM’s Master Data Management & Information Integration and Governance business and IBM’s Data Warehousing & Analytics business responsible for the strategy, directions and operational management of the Warehousing portfolio.

You were originally interested in mathematics and linguistics. What inspired you to pivot towards computer science and AI?

When I was young, I had an interest in everything else but technology. As you noted, I was mainly interested in Spanish and foreign languages and linguistics. My parents were both engineers and entrepreneurs and they instilled in me a love of science. Technology has that intersection point of being able to affect every industry and every aspect of our daily life. After my first class in college, I knew this is where I belonged.

While I followed my passions toward a career in technology, unfortunately many young women choose non-STEM careers paths. That's why I am so excited that for the third year in a row IBM is honoring exceptional women business leaders for their pioneering use of AI at companies across the globe through our Women Leaders in AI initiative. Other young women need to be able to see themselves in these Women Leaders in AI and have confidence and community to encourage pursuits in technology, and AI.

You’ve had an incredible career at IBM, could you share with us part of this journey?

People often ask me why I have such a long career here at IBM. At IBM, I have been able to grow my skills in product areas and functional domains. I started out in our systems management team as a developer and have since progressed through data, analytics, and AI in functions of product management, sales, and services.

It has been highly valuable for my career to be able to start in one area, expand my knowledge about a particular domain, then transition to another area. I’ve learned from each move, developing a solid foundation critical to my current role helping businesses adopt new technologies.

Through each role I have found it helpful to focus on 3 areas to propel growth: (1) Find a community to support the learning and growth areas needed for the new role. This can be through existing groups, mentors, and even reverse-mentoring. (2) Be curious. Always learn from hands on work, studying and practicing your craft and continually learning. (3) Be confident in your abilities, probably the most important aspect.

You’re currently the Vice President of Expert Labs, IBM Cloud and Cognitive Software. Could you share with us what this role entails?

The Expert Labs team helps organizations fully realize the potential of artificial intelligence in keeping their business strong in a highly competitive landscape. I lead a team of over 2400 highly technical experts focused on advising, architecting and delivering client success with data, automation, and other AI use cases.

We work with clients to make sure their technology projects are successful by helping them to understanding their goals and guiding them along each step of their journey to business transformation.

You’ve previously stated that to be successful with AI, it all starts with the data. Could you elaborate on this?

Like you said, to fulfill the promise of AI, organizations need to start with their data. AI has the potential to transform how businesses operate and deliver value, however, many continue to struggle with overcoming data complexity, talent scarcity and a lack of trust in AI systems. As we're celebrating diversity in AI, I'll focus on two key questions that I think are key developing trust in data and AI systems: is my AI fair and is my AI explainable?

To ensure fair AI, we must make certain that the data the models are built upon is fair, and that the models themselves are designed to detect and mitigate bias as new data is introduced. The mandate to remove bias has become more urgent amid our intensifying global conversation around racial and economic justice. When we ensure that AI is fair, it can be an excellent tool for mitigating human bias.

If we can’t explain why AI is making certain decisions, fears of a “black box” of mysterious algorithms can make it impossible to engender trust. Industries like financial services, healthcare and insurance, present an enormous opportunity for deploying AI at scale, however it requires highly sensitive data to make decisions that significantly impact people’s lives. It’s critical that customers understand how these decisions are being made and why. To ensure that the best and most equitable decisions are made, and to satisfy regulators, we need an auditable data trail that gives accurate answers.

Ensuring that AI and the data it's built on are fair and explainable is the lens through which we need to view successful AI projects. My belief is that a diverse group of backgrounds and lived experiences is critical to that process.

You're very involved in IBM’s Women of AI program. Could you tell us more about this program?

The trailblazing women included in our annual list of Women Leaders in AI have created an environment where more and more women can push boundaries in the technology industry, make their voices heard, and open doors for the next generation. It's critical to celebrate and share these stories to keep that momentum, which is why we are honoring 40 amazing women from 18 countries around the world who are shaping the future of artificial intelligence and how advancements in natural language processing, automation and trustworthy AI can be used to help organizations better predict outcomes, automate processes and drive new efficiencies.

The list honors leaders from AdMed, The Ad Council, The Clorox Company, City of Austin, EY, Ford Motor Company, Lloyds Banking Group, Mitsui Chemical, Telstra, Vodafone New Zealand, Westpac, and many more. These incredible leaders are breaking new ground using AI to improve the effectiveness of advertising, empower farmers with essential crop forecasting tools, helping to improve food safety, keeping constituents up to date during the COVID-19 pandemic, and much more.

How important is having a diversified team of minorities and women in order to avoid AI bias?

In the span of a year, the COVID-19 pandemic upended generations of working women, with more than 5 million just in the US losing or leaving their jobs. In fact, new research from the IBM Institute for Business Value shows that fewer women today hold senior vice president, vice president, director and manager roles today than they did in 2019. We created the annual Women Leaders in AI program in 2019 to help encourage increased diverse participation in the field and provide honorees a network for shared learning. My hope is that others will read the stories of these remarkable leaders and find inspiration—and, just as important, see glimmers of themselves.

Additionally, the importance of having diversified thinking behind innovations in AI cannot be understated. Diversification of thought is key to the development of AI. We've seen that as more variety of thought goes into creating the technology, there is a greater likelihood of mitigating bias, promoting ethically operated AI and improving confidence in AI systems. Ultimately showing that diverse work practices enable a deeper trust in the programs that are developed, leading to better economic returns and benefits for all. We celebrate the steps that have been taken to create a more diverse and inclusive AI field, as we continue to strive towards making it that much better.

What are some things that parents can do to inspire girls to be more interested in both computer science and AI?

Two things standout to me – curiosity and mentoring. In my experience, the most essential ingredient for success in AI is a culture of curiosity and continuous learning. In tech, broadly speaking, the average life span of skills and information is three to five years. With AI, it’s 12 to 18 months. The technology is moving so quickly that a curious mindset and hunger to learn are more important than any particular skill. Fostering curiosity can start at a young age.

For girls interested in technology, support systems and mentoring are critical. It’s all about creating a culture where people feel they can continually stretch themselves. I learned this on a personal level several years ago, when I sent my son and daughter to coding camp together. My son loved it, but my daughter came home and declared, “I don’t want to code anymore, Mom.” After a chat, I realized that she was the only girl in her class. So I signed her up for a special program where girls code together. She had fun, gained confidence and can now hold her own anywhere — including in a room with dozens of boys building Minecraft modules.

What can enterprises do in order to attract more women?

If we want to increase the numbers of women in AI—and increase diversity in the field across every dimension—we must celebrate the diversity that exists. We must make sure that people representing different groups and backgrounds have supportive tech communities where they feel comfortable asking questions, making mistakes and venturing into unfamiliar territory—all necessary parts of learning. As IBM celebrates 40 women who are true pioneers, we are also connecting them to each other as a new network for learning, sharing and supporting one another. Based on the results found in the new research from the IBM Institute for Business Value, we've proposed a roadmap for change:

  1. Pair bold thinking with big commitments, making gender equality a formal business priority.
  2. Apply specific crisis-related interventions. Benefits like backup childcare support and mental health resources and flexible work locations and schedules can be key.
  3. Create a culture of intention and insist on making room. Employers and managers need to take an empathetic and inclusive approach toward their employees.
  4. Use technology to accelerate performance. That means hardwiring fairness into screening, use digital tools for communication and feedback to surface what’s working and what’s not, and invest in collaborative tools and teaming practices that allow women and men to engage effectively in physical and remote environments even after the pandemic abates.

Thank you for the great interview, readers who are interested should visit IBM's Expert Labs, or IBM's Women Leaders in AI program.

Antoine Tardif is a Futurist who is passionate about the future of AI and robotics. He is the Co-Founder of a news website focusing on digital assets, digital securities and investing. He is a founding partner of unite.AI & a member of the Forbes Technology Council.