Person
1 episodes · 15 quoted claims
JoAnn Stonier helped Mastercard become compliant with the European Union's General Data Protection Regulation (GDPR) while she was its chief privacy officer.
“I helped, um, the company become GDPR compliant, which really was, I think, uh, the moment in time when many, many companies were coming to the place where they needed to operationalize a lot of data risk.”
Mastercard uses a process called privacy by design to build privacy and security into its products.
“We had a whole process called privacy by design. We still use that process.”
Before Mastercard, JoAnn Stonier was chief privacy officer at American Express, where she also held other roles.
“I was previously the chief privacy officer for American Express.”
JoAnn Stonier teaches business strategy in the design management master's program at Pratt Institute.
“I teach in their design management master's program, and I teach business strategy”
Stonier says 1910 United States voter rolls inherently omit women and people of color, so they suit some inquiries but not others.
“you need to know that inherently that data set is going to miss women. It's going to be missing people of color.”
Stonier suspects airlines sized overhead bins using average heights that did not account for enough women, as an example of averages that are not fit for purpose.
“it was on very average heights, okay, but the average height did not include enough women.”
Stonier says the first generation of data analytics used rules that acted when a set condition appeared in the data.
“we were creating algorithms or analytic questions that really asked the question of this is the question. If this condition exists in the data set, then do something, right?”
Stonier says machine learning starts from the data set and asks what it reveals, in contrast with rules-based analytics.
“Instead, we take the data set and say, what does the data tell us? What does the data tell us, and what does the machine then learn from them?”
Mastercard's data teams check early model outputs for model drift and accuracy, so input inequities are not amplified.
“we're gonna be looking for the model drift. We're gonna be looking to see is this an accurate output?”
Stonier says teams should trace data lineage, quality and consistency before using data sets in analysis.
“you've got to back up the data food chain to understand your data lineage, understand the quality, the consistency of the data sets”
Stonier says data can be restricted by contract or by its type, such as financial data, which constrains how Mastercard may use it.
“Can we not use the data because it's contractually restricted, or it's restricted because it's a certain type of data, it's financial data, right?”
Stonier's team at Mastercard includes people with both law and engineering degrees, who translate between business, technical and regulatory domains.
“I have lawyers and engineers on my team, right? They have both degrees.”
Stonier says that when COVID began, many merchants moved online and sought data and guidance to make decisions.
“So many merchants were going online. If they didn't have a huge presence, they needed to create one.”
Stonier gives designing a look-alike fraud model that uses only synthetic data as an example of a constraint-driven data challenge.
“the outcome is to design a look-alike model for fraud that only utilizes, you know, synthetic data, right?”
Mastercard uses a specific framework for ethical AI and is applying it to different contexts.
“So we have a very specific framework that we're using around ethical AI, but then we're really getting into the nitty-gritty around different kinds of context.”