We talk with Kishawna Peck, Founder & CEO of Toronto Womxn in Data Science, about the diversity gap in the field of data science. And we explore opportunities for Canadian businesses and research organizations to help start to close that gap.

The views expressed in this article are those of the interviewee and do not necessarily reflect the position of RBC or Borealis AI.

What are some of the causes of underrepresentation of minority communities in data science?

Kishawna Peck (KP):

There are a number of factors that may create barriers to women and minorities joining the data sciences. I think one big factor is simply a lack of data literacy. I was already in my mid-20s when I first started to think about data. I think we’ve also made data science seem very specialized and that makes many feel they are locked out of the field. But the reality is that there are a wide range of roles available in data science; you could be a lawyer that specializes in the field, for example. Even if we overcome those barriers, there is still a lack of transition programs available to help women move into the data sciences. And then once we get into the workforce, sexism and intersectional discrimination can arise which then drives women out of the data sciences.

And what impact does this lack of diversity have on the sector?


There is a mountain of literature focused on the biases in the sector. And it’s not just the bias in the data. There are some data sets that aren’t reflected at all, simply because they didn’t really affect the dominant group responsible for collecting and analyzing it. So I firmly believe that you need to have an inclusion and equity lens if you want to create technology that doesn’t further amplify biases. But in the age of automation, the reality is that women are at risk of being left further behind. So there is real urgency around increasing the number of data literate women in the marketplace, upskilling those already working in the sector, and creating more inclusive teams.

What can Canadian companies do to improve diversity in their data science teams?


To me, one of the big challenges is actually retention. Women who join the data sciences do not always stay in the field very long. We’re basically running back and forth with a leaky bucket. We need to stop those leaks so we can retain more women in the field. That requires organizations to have truly inclusive environments and company cultures. It means keeping abreast of the ethical considerations being raised by different groups and working hard to ensure you are not unintentionally excluding them. And it requires companies to govern the products and models they already have in market to adjust them as needed.   

And how can Canadian organizations help nurture diversity in the wider ecosystem?


I’ve been encouraged to see a lot of new corporate-led programs that focus on helping women, minorities and underrepresented groups become more data literate. The RBC/Borealis AI Let’s Solve it program is a great example of a leading Canadian company actively working to drive diversity in the field. We need more programs that help women and minorities to discover data sciences. We also need more programs for those transitioning careers into data science. There are lots of privately-run ‘boot camps’ being promoted on the internet… some of dubious value. But there are very few robust programs with standardized curriculums supported by corporate Canada.

How does Toronto Womxn in Data Science help?


Our vision is to empower and inspire one million women to become data literate. And we have a number of programs to support that mission. We have our annual conference. We have a podcast series aimed at youth and those transitioning careers. We have a data and media club where we help develop study guides for movies that deal with data topics. This winter we’ll be launching a 12-month fellowship program where women would be able to build data products, learn the AI product development cycle, and develop all the different skills they would need to build data products and services.

And what inspired you to start Toronto Womxn in Data Science?


It was an accident! Every quarter, I would search for workshops and conferences that could help me improve my data science skills. One day, I found a conference that was just for women in data science. But it was in the United States and there were no equivalent events up here in Canada. So I decided I should host one. I thought it would just be a handful of people at the back of a public library somewhere. But we ended up with more than 100 participants and a roster of great speakers. It was a great event. Standing at the back of the room, however, I couldn’t help but notice that there was a distinct lack of diversity in the audience and in the panel. So we made a commitment to be more intersectional in the way we organize and plan our events.

What’s next for your organization?


We’ve seen a lot of demand for our events and programs. And that demand is growing not only in Toronto, but across Canada and into the US. So we’re looking at how we can better represent a wider geographical area. We’re also really excited about our 12-month fellowship kicking off this winter. It will have a fantastic multiplier effect that will allow us to have even more impact with our programs.

Interviewee Kishawna Peck's Headshot

Interviewee Kishawna Peck’s Headshot

About Kishawna Peck

Kishawna Peck is the Founder & CEO of Toronto Womxn in Data Science, an organization with an ambitious goal to help a million women become data literate, increase the recruitment and retention of women in data roles, and increase the use of inclusive design practices. Kishawna spent years leading corporate data programs as the Manager of Data Analytics for a large IT consulting company and as a Product Data Analyst for a financial services firm. To learn more about Toronto Womxn in Data Science, to participate in one of their events, or to explore sponsorship opportunities, visit their website at Toronto Womxn in Data Science.