Saishruthi Swaminathan on Paving the Way for Women in Data Science
Even if you haven't been closely watching the tech sector for the past 20 years or so, there's a good chance that you're already aware of just how important data and AI have become, not only to tech devices and services but to our lives in general.
Both widespread data collection and various applications of AI have been the subject of heated debate in recent years.
While these instances have not been representative of all data collection and AI efforts, there have indeed been examples of these technologies being used in unethical or unsavory ways.
Forward-leaning minds in tech, ethics, and philosophy have been hard at work discussing potential ways of avoiding bad faith applications of extremely powerful technologies.
For Saishruthi Swaminathan, data scientist and ethical AI advocate, the main answer to the question of how to improve uses of these technologies is to bring a wider range of people into data science on the professional level.
Swaminathan has been a major supporter of women in data science, and this support has included extensive involvement in various initiatives and events whose goal is to give both young and adult women the tools and understanding they need to contribute in a positive way to these fields.
In our interview with Swaminathan, she focused on her efforts to eliminate discrimination in data science, especially with regard to hiring practices.
One of her initiatives seeks to completely remove bias pinned to gender, background, appearance, etc. during the job search and hiring process, specifically within tech and data science.
In addition to her AI and data science expertise, Swaminathan is also a highly successful public speaker, having given talks at more than 50 events.
The Idea Trader would like to thank Swaminathan for speaking with us, and we hope you, the readers, enjoy the discussion.
You've committed yourself to helping women in the data science community. Can you tell us more about that?
Swaminathan: Sharing experiences from your own life is more powerful than statistics and numbers. Opinions here are from my own experience.
We live in a society where that is constantly pushing you to be someone else. It may even go to an extent where a community defines a thought process.
Let me give you an example from my own life. I was gifted dolls and dresses in my childhood, whereas my brother used to get toy cars and tech products. The response I get when I say this to people is, so what?
We are indirectly reinforcing in the minds of young girls that tech is not for them. So every action we perform consciously or unconsciously is affecting young minds.
My Initiative AI is for all, and what appears to be a complex topic is simple when broken down into a small, relatable piece of information. I lead workshops, talks, and sessions focusing on breaking down data science and trusted AI concepts into simple terms with examples that are more relatable to real life through initiatives focused on Women Empowerment like Women Who Code and Girls Who Code.
Are your efforts in this area influenced by your own experiences entering data science on the professional level?
Swaminathan: When I was searching for a job, I experienced discrimination where people outright said that they didn’t want to give me an opportunity based on my gender, background, and appearance.
My initiative came from the idea to create a bias-mitigated recruiting platform, Saira. Through this, we aim to create a recruiting platform that functions purely based on the skills and personalities of job seekers.
What do you think is one of the biggest problems with how women within data science are treated?
Swaminathan: “You don’t have to be perfect. It’s ok to make mistakes.” If we take this advice to heart, I am sure we will see a lot more women breaking into the data science field.
Women are expected to be perfect all the time, and breaking this stereotypical mindset is essential. We need to create a space where eyes are not judgmental and biased. Diversity is critical in data science.
Lack of diversity can lead to bias penetrating AI systems. It is crucial to make everyone in the community feel empowered. Saira, the Bias Mitigated Recruiting Platform Initiative, came into life to overcome this imbalance.
Do you feel that more and more women, especially younger women, are becoming interested in data science in some form?
Swaminathan: Of course, women are naturally gifted with creative minds. This is a great asset when dealing with data. The more creative you are, the better results you can get from pure data.
What was the biggest challenge for you when trying to start your career?
Swaminathan: The story behind my bias-mitigated recruiting platform proposal answers this question. The simple answer is bias, bias towards gender, community, appearance, and educational background.
At one point, I stopped applying for jobs and went to hackathons to approach recruiters with projects I'd done. I usually feel hopeful the moment they get my resume.
What kept me going was my persistence and attitude of dedication. Finally, I was able to land my dream job and do what I love doing.
Have you enjoyed speaking on the topics of AI and data science?
Swaminathan: I love relating data science topics with real-world examples. One example that I usually use when talking about neural network basics is making the audience think why and how they identify a cat as a cat.
First, people start expressing how they identify with features like eyes, ears, talk, and body structure. Then I relate how we learn with how machines learn. I create examples like this, and when I present examples along with code, my audience will love those.
I started with ten people attending my talk. Recently, I've had over 10,000 people attending my sessions. If we are trustworthy, respect people’s time, and hold on to our authentic style, people will understand our efforts.
It's magic to experience a connection with the audience. Over the past three years, I have given over 55 talks. Community is an integral part of life.
What was one of the most difficult parts of your career and how did you overcome it?
Swaminathan: The tricky part was understanding and realizing what I loved and enjoyed doing. I started my career as a software engineer.
Though I performed well, I was missing the 'wow factor.' Then, in the middle of my master’s internship program, I got an opportunity to be a part of the data science team. Trust me, I felt that data was speaking to me. I enjoyed every single bit of it. My passion gradually became my profession.
How would you describe the professional legacy that you'd like to leave behind?
Swaminathan: With passion, hard work, and determination, anything is possible in life. Believe in yourself and keep doing what you love.