Mastercard Chief Data Officer on AI Ethics, Diversity and Innovation

According to JoAnn Stonier, chief data officer at Mastercard, the last thing organizations want to do is invest heavily in developing artificial intelligence (AI) tools for use across the enterprise, only to find that their insights are “skewed” – powered by data sets that are incomplete, inaccurate, inconsistent, or biased.

“You need to have a strong, ethical approach to your use of data, a strong commitment to really truthful and accurate, good quality information so you don’t have the wrong information in your products and solutions,” she told VentureBeat. If you do, she explained, “you’re getting in line with what regulators want. You will work in the same interest as your customers and their consumers and individuals.”

That sense of responsibility around data leads Stonier, who will become part of the Women in Data & AI breakfast panel at VentureBeat’s in-person summit at Transform on July 19 in San Francisco, along with Ya Xu, VP of engineering and chief of data at LinkedIn, and Molly Parr, VP, product, digital customer experience, enterprise products and platforms at Capital One. Sponsored by Capital One, the panel will focus on how the rise of women in data and AI-focused fields has helped shed light on unconscious biases, and may lead to a better understanding of how to avoid them.

A set of skills for data privacy and bias issues

Stonier and her team are helping Mastercard develop a data strategy and work on data governance, data quality and data compliance. Her organization also enables artificial intelligence and machine learning and helps design and operate some of the company’s enterprise data platforms.

Stonier’s unique background prepared her for the broad role of Chief Data Officer at a global leader like Mastercard. Trained as a lawyer, she was previously an auditor and accountant and obtained an early degree in computer science.

“When I went back to law school, I didn’t know that all my skills would be put together into a really nice package when the data world came up,” she said. “One of the CEOs I worked for at American Express came up to me and said, there’s a new area of ​​law that I think will be perfect for you called privacy. So privacy and data really brought all my legal skills together.”

The power of data principles for AI innovation

A few years ago, she said, MasterCard developed a set of data responsibility principles around privacy and security to guide innovation. “With the rise of AI, we are adding a principle to make sure we use inclusive data sets, have inclusive employees, develop our algorithms and look at the results we develop,” she said. “We want to make sure we don’t have flats in the data we use or the methodologies we use to create our algorithms, and that our results are fair for whatever problem we’re trying to solve.”

AI and machine learning (ML) are important tools for Mastercard, including through the use of algorithms intended to detect fraud. “We’re committed to constantly refining our information so that we can detect fraud on a very specific level and also not have denials that are truly legitimate transactions,” explains Stonier.

Andrew Ng, she added, talks about data as the “food for AI.” “So are our data sets the right ones to use — are they representative of the populations we’re trying to serve?” she said. “And what about the algorithms we make, are they structured, is the algorithmic research the right one? And do they have inherent biases about how we structure them?”

Stonier explained that Mastercard has a review process that includes the data design team, data quality team, and data science teams working together in an iterative process as AI and ML methodologies are developed. When applied, they are first run in a test environment before being put into production.

“Then we look at the results — we look at the models for drift and anything else that will show us that we’re not getting the results that make sense,” she said. “If we get results showing that a particular population is affected and it doesn’t make sense, we go back to the drawing board.”

Mastercard’s next-generation network

Stonier also works closely with Ed McLaughlin, president of operations and technology at Mastercard. The two are working to change the way the company’s infrastructure works, including a project they loosely call a next-generation network.

“It’s very data-driven in how we think about the information we need to process around the world, so we’re working closely together to think through the design because we know Mastercard’s data needs will change in the future now that we’re a multirail network,” she said.

The company will also have other rules to keep in mind. “There will also be different ways we think people will interact in the new virtual world — with virtual reality and augmented reality,” she said. “We’re going to have to capture all that information in different ways.”

Stonier emphasized that she does not consider being a woman first in her career. “I just see myself as a business executive, and as a data professional and now definitely as an AI professional,” she said. Still, she admits there are still prejudices — and far too many meetings where she’s the only woman in the room.

“That has to change, and especially for AI we need different perspectives,” she said. “Prejudice can creep in and different perspectives are very important in designing machine learning and artificial intelligence for the next generations.”

Events like transformWomen in AI breakfasts are important, she added, because “The last thing we all want, and I think this applies to both men and women, is to develop artificial intelligence that learns from itself and the female perspective exit, or any perspective,” she said. “We want our machine learning to reflect all aspects of society — so I think breakfast like this reminds us that we need to develop AI with a 360-degree lens that captures all the different really understands aspects of society.”