Designing a Better Future: Mastercard’s JoAnn Stonier

**Sam Ransbotham** (0:02)
We hear a lot about the bias AI can exacerbate, but AI can help organizations reduce bias too. Find out how when we talk with JoAnn Stonier, Chief Data Officer at Mastercard.
Welcome to Me, Myself, and AI, a podcast on artificial intelligence in business. Each episode, we introduce you to someone innovating with AI. I'm Sam Ransbotham, Professor of Information Systems at Boston College. I'm also the Guest Editor for the AI and Business Strategy Big Idea Program at MIT Sloan Management Review.

**Shervin Khodabandeh** (0:34)
And I'm Shervin Khodabandeh, Senior Partner with BCG, and I co-lead BCG's AI practice in North America. And together, MIT SMR and BCG have been researching AI for five years, interviewing hundreds of practitioners and surveying thousands of companies on what it takes to build and to deploy and scale AI capabilities across the organization and really transform the way organizations operate.

**Sam Ransbotham** (1:03)
Today, we're talking with JoAnn Stonier.
JoAnn is the Chief Data Officer at Mastercard. JoAnn, thanks for taking the time to talk with us today. Welcome.

**JoAnn Stonier** (1:10)
Thank you. Happy to be here.

**Sam Ransbotham** (1:12)
JoAnn, let's start with your current role at Mastercard. Can you give us a quick overview of what you do?

**JoAnn Stonier** (1:18)
Sure. I'm currently, as you said, the Chief Data Officer for the firm.
And me and my team are responsible for ensuring that Mastercard's information assets are available for information while navigating current and future data risk. So my team has a very broad mandate. We work on helping the firm develop our data strategy and then work on all the different aspects of data management, including data governance, data quality, as well as enabling things like artificial intelligence, machine learning.
And then we also help design and operate some of our enterprise data platforms. It's a very broad-based role. We work also on data compliance and how do you embed compliance and responsible data practices right into product design. We start at the very beginning of data sourcing, all the way through to product creation and enablement. It's a lot of fun.

**Sam Ransbotham** (2:09)
Because our show is about artificial intelligence, let me pick up on that aspect.

**JoAnn Stonier** (2:12)
Sure.

**Sam Ransbotham** (2:12)
Can you give us examples of something you're excited about that Mastercard is doing with artificial intelligence?

**JoAnn Stonier** (2:18)
Oh my gosh. I've had so many conversations just this week about artificial intelligence.
Most of them centered on minimization of bias as well as how do we build an inclusive future. The conversations that really excite me is how the whole firm is really getting behind this idea and notion. I've had conversations with our product development team and how do we develop a broad-based playbook so that everybody in the organization really understands how do you begin to really think about design at its very inception so that you're really thinking about inclusive concepts. We've had conversations with our people and capabilities team or what's more commonly known as human resources, talking about the skill sets of the future. How are we going to not only have a more inclusive workforce at Mastercard and what do we need to do to provide education opportunities both inside the firm but also outside the firm so that we can create the right kind of profile of individuals so that they have the skill sets that we need, but also how do we upskill, how do we really begin to create the opportunities to have the right kinds of conversations?
We're also working really hard on our ethical AI process. So there's so many different aspects of what we're doing around artificial intelligence, not just in our products and solutions, in fraud, in cyber, in general analytics and in biometric solutions around digital identity, that it's just really an interesting time to do this work and to really do it in a way that I think needs to last for the generations ahead.

**Shervin Khodabandeh** (3:46)
JoAnn, that's really interesting. I'm particularly excited that one of the things you talked about is use of AI to prevent bias. I mean, there's been a lot of conversations around the unintended bias of AI and how to manage it. But I heard you also refer to it as a tool that can actually help uncover biases. Can you comment more about that?

**JoAnn Stonier** (4:11)
Yeah, but it's hard, right? This is a lot of hard work. I do a lot of conversations, both with academic institutions, other civil society organizations. We're at early days of AI and machine learning. I think we're probably at generation, maybe 1.5, heading into generation two. But I think the events of this past year have taught us that we really need to pay attention to how we are designing products and solutions for society.