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.
And I'm Shervin Khodabandeh, senior partner with BCG.
And I call it BCG's AI practice in North America.
And together,
MITSMR 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.
Today we're talking with JoAnn Stonier.
JoAnn is chief data officer at MasterCard.
JoAnne, thanks for taking the time to talk with us today.
Welcome.
Thank you, happy to be here.
JoAnn, let's start with your current role at MasterCard.
Can you give us a quick overview of what you do?
Sure, 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.
Because our show is about artificial intelligence
let me pick up on that aspect.
Can you give us examples of something you're excited about
that MasterCard is doing with artificial intelligence?
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 a more commonly known as human resources.
Talking about the skillsets 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 upscale,
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 and cyber,
in general analytics and in biometric solutions
around digital identity.
It's 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.
JoAnn that's really interesting.
I'm particularly excited
that one of the things you talked about is use of AI
to prevent bias.
There's been a lot of conversations
around the unintended bias of AI and how to manage it.
But I heard you also referred to it
as a tool that can actually help uncover biases.
Can you comment more about that?
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.
Right, we're early days of AI and machine learning.
I think we're probably at generation maybe 1.5
heading into generation 2.
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.
And that our data sets are really important
in what are we feeding into the machines
and how do we design our algorithmic processes
that we also were feeding into the intelligence
and what is it going to learn from us?
And so I try to explain to people
our first generation of data analytics.
We were creating algorithms or analytic questions
that really asked the question of, this is the question,
if this condition exists in the dataset
then do something, right?
So we were looking for the condition to exist
in the data set and then we were acting-
Rules-based.
Right, rules-based, exactly.
Artificial intelligence and machine learning
actually flips that a little bit on the head.
Instead we take the dataset
and say: what does the data tell us?
What does the data tell us
and what does the machine then learn from that?
Well, the challenge of that though
is if you don't understand what's in the data, right?
And the condition of that data
as you bring it into the inquiry,
and you don't understand how those two things fit,
then you wind up with a biased perspective.
Now, it's unintended, it's inherent, right, to your point.
But nevertheless, it's something that we have to back up
and readjust our lenses as we begin to look at our processes
around artificial intelligence.
So we start with the data sets.
And we understand that data sets are going to have
all sorts of bias in them and that's okay.
First of all, bias always gets
kind of a very gripping reaction, right?
I use the example all the time,
if you go back to like the 1910 voter roles
in the United States,
that's a valid data set.
You may use that for whatever purpose you may have
for evaluating something that happened in 1910 or 1911.
But you need to know that inherently,
that data set is going to miss women.
It's going to be missing people of color.
It's going to be missing parts of society.
As long as you know that
then you can design an inquiry that is fit for purpose.
The problem is if you don't remember that
or you're not mindful of that,
then you have an inquiry that's gonna learn off of a dataset
that is missing characteristics that's going to be important
to whatever that other inquiry is.
Those are some of the ways that I think
we can actually begin to design a better future.
But it means really being very mindful
of what's inherent in the dataset,
what's there, what's missing, what also can be imputed.
We can talk about that,
what kind of variables can be created by the machine, right?
It can be imputed as well.
And so all of those things
are things that we're looking at at MasterCard.
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 contexts.
So I wanna build on that
because I think it's a very critical topic
to so many of our listeners.
So you talked about as long as you know
you could look for it.
But then the things you don't know
and then you'd find out in the hindsight,
how do you prepare for those?
Well, I think you have to be aware
again, for what purpose to what end.
And now let's go look at the datasets.
And this is where you've gotta backup the data food chain
to understand your data lineage.
Understand the quality, the consistency
of the data sets that are going into the analysis.
And then this is not easy.
So nothing I'm saying, I want everybody to hear me clearly
nothing I'm saying is easy, right?
Everything requires a lot of scrutiny and time.
But that's easiest if you're doing it yourself.
But if a vendor is doing this
and is presenting this to you as a combined solution,
you need to be even more curious
about what is going into this recipe.
Every one of these elements becomes super important, why?
Because if we do it when we're building it,
those of us who are the data scientists and the data expert,
the data designers, right?
We understand it because when we're gonna be looking at
the information models and the outputs as they come out
in the first generations,
we're gonna be looking for the model drift.
We're gonna be looking to see: is this an accurate output?
Is this truly the result
or is it because of some inequity in the input?
Which is okay if it's accurate, that's okay.
Because the machine's gonna then start learning
and we're gonna put more data and more information.
But if it's incorrect and we haven't caught it
it could get amplified to the incorrect result.
And it could have,
and this is what we also really care about
-: that's when we get to the output analysis -
it could amplify really bad outcomes.
And the outcomes can be significant or insignificant
depending upon if it's being designed for an individual
or if it's being designed for groups.
Or if it's just being something...
I talk about how I suspect
that when the airlines were first modeling
the size of the overhead bins,
it was on very average heights, okay?
But the average height did not include enough women.
How many ladies climb on seats to get their luggage
at the very back of the bend
or need to rely on one of you nice gentlemen
to grab our overhead suitcases down to assist us, right?
Averages sometimes are the worst result
because they're not fit for purpose.
I think you're talking about injecting an entire new layer
in the data analysis or data usage process, right?
Maybe. (chuckles)
Because, was it not the case, Sam
that 20 years ago people thought of data as neutrals.
Like data is neutral, you use what you want.
But data could be potentially harmful.
And I think what you're talking about is,
you're trying to create a mindset shift
that says it's not just data comes in, answers go out.
But you have to be mindful of why you're using it,
how you're using it.
And this mindset shift
just permeates not just your organization
but also you're saying your vendors
and other parts of the ecosystem.
It's a huge undertaking.
It is a huge undertaking.
So those are some challenging aspects,
what's rewarding?
What kind of exciting things happen?
These seem a little bit sort of like,
"Oh gosh, we have to worry about this,
have to worry about that!"
Get us excited about the fun part.
Oh, I think all of this is fun, I do.
But I'm a data geek, right?
So what's fun is actually seeing my firm
come to life around this.
These are actually very exciting conversations
inside of the firm.
I don't mean to make them sound
like just risk conversations at all.
It's intellectually challenging work.
But we all agree that we think
it's going to bring us to better places.
And already is bringing us to better places
in product design.
You talked about the workforce aspect of it
and that technology is necessary
but is as far from sufficient.
We heard from one of our other speakers,
the president of 1-800-Flowers about learning quotient,
the desire to wanna learn.
How do you bring all of that together
in terms of the future of the workforce
that you guys are looking at
and the kind of talent and skillset and attributes
that are going to be successful in different roles?
Are you taking actions in that direction?
We really look for people
who also can design controls and processes
and make sure that we can navigate those.
We live in a world of connected ecosystems
and so we're only as good as our partners.
But it means those handshakes, right?
Those connections are super important to understand.
And so how do you make those inquiries
of other organizations
so that you can create those connected ecosystems
that are only going to grow in size and scale
for the future?
And how do we help design those?
How do we be the leaders in designing some of those
is really, really important for us.
So it really is this meshing.
I talked about being like a three-sided triangle
of data skills-
Is there a different kind? (all laughing)
But we sit in the middle, right?
We sit in the middle of the business.
We sit with technology skills.
As well as there's an awful lot of policy
that we need to understand often.
It's the other design constraint
of where are law's evolving, where is the restrictions?
The other type of restrictions that we need to know.
Is it gonna be on soil?
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?
It only can be used for one specific type of purpose.
Or it can only be used at an aggregated level for example.
Or it can be used at a segment level.
Those types of restrictions as well at that compliance level
also needs to be understood.
So sitting in the middle of the middle
is sometimes really hard.
So that's why you have to be able to admit
you don't know one aspect of that triangle.
So you mentioned the word design,
a couple of times at least. (JoAnne laughs)
Here it comes. (Sam laughs)
Can you tell us little about how you got to your role?
Because I think the design thinking
is coming from your background.
Can you share with us how you got to where you are?
I think it's probably easier
if I just go quickly backwards.
So prior to being the chief data officer
I was the chief privacy officer for the firm.
Which was a lot of fun.
I enjoyed that role immensely.
I helped the company become GDPR compliant.
Which really was, I think the moment in time
when many many companies were coming to the place
where they needed to operationalize a lot of data risk.
And we had been on that journey.
We had a whole process called privacy by design.
We still use that process.
Lots of companies use that phrase
because it's a regulatory phrase.
But we had already been
looking at our products and solutions.
To try to understand how could we embed privacy and security
into their very design and into their fabric.
And we still do that to this day.
But as we were doing the compliance work
and planning it out for the GDPR, the European privacy law,
we were realizing we were gonna need additional platforms
and systems to be built.
And as I was doing that
I kept saying to anyone who had listened,
"Who's gonna own all the data
because I really need somebody to speak with?"
And they just kept patting me on the head saying,
"You just keep designing everything and it'll be fine."
So here we are that I'm the first chief data officer.
But it was kind of an expansion
and then a severing of a lot of the work
that I had been doing.
But I came to be the chief privacy officer.
I was previously the chief privacy officer
for American Express.
I came to that role after a pretty good size career
at American Express as well.
Working in a variety of roles
I understood how that firm works.
So financial services
is something I've been doing for a while.
But the design piece comes in because of 9/11.
I had the misfortune of being downtown that day.
And the American Express building is right across the street
from was the World Trade Center.
So saw a lot and lost colleagues that day.
And we were all relocated for several months
while the building was repaired.
And so on New Years of the next, I don't know
it was probably like January 4th actually of 2002.
I was thinking, well, there's a lot of people
who were not alive that New Year's.
And I thought, well, what can I do?
What haven't I done in my life
that I really want to go accomplish?
And so I decided to go back to design school.
This is after law school.
So I got my law transcripts, were part of the process.
Which was fun getting them to send my transcripts
to a design school.
But I got a design degree.
And I'm really glad I did
because it's made me a better business person.
And it's given me a whole different way of thinking
about problem solving.
And then I've had the good fortune.
So I do, yes I do interior design work.
And yes I can help you with your kitchen and your bathroom
and all the other things you wanna do during COVID.
I've helped several colleagues pick out tile
and other things.
All right, we'll talk after the podcast about that.
We can talk after the podcast about that.
But I also then had the good fortune
to meet the Dean at Pratt Institute.
And I teach in their design management master's program.
And I teach business strategy
and I've taught other courses in that program.
And that's also helped me
really evolve my design thinking as well.
And so all of these things,
yes, I have a law degree, yes, I'm a data person.
But having the design thinking as well
really makes you not think of things as problems.
So just constraints around
which you have to design things
and that they will shift over time.
So it's no different than the electric is over here
and the plumbing is over here
and you only have so much space.
So how can you utilize this, right?
It's the same thing for,
I can only use the data in this way
and I want to achieve an outcome, how do I do that?
And so it is the same kind of strategic toolkit.
You just kind of flip it for,
"Okay, well, the powder room has to be only this big."
(chuckles) So that's one challenge.
Or the outcome is to design a look alike model for fraud
that only utilizes synthetic data, right?
Or something along those lines.
But it does kind of give you a little bit of a can-do spirit
because you figure something has to be possible.
Because there's raw material in the world.
So that's kind of how I approach things.
Very well said.
That's a very weird triangle.
Seems the triangle is a very weird-
My triangle is probably very weird, I get a lot.
Well, that's a bit of a theme though.
Other people we've talked to
they're bringing in these experiences from other places.
And very often they're not super technical things.
We're talking about artificial intelligence
and you might think that it would quickly go down a path
of extreme technical.
But I think it's just as likely to go down a path of design.
Yeah, I know and what we find on my team
and it generally is that if you find people
that have kind of that combination
of right brain left brain.
So I have lawyers and engineers on my team,
they're both degrees.
It's a really interesting match
of being able to translate the business to the technical,
the technical to the business, right?
Translating the regulatory to the business
or the regulatory to the legal.
It kind of works as a giant translator role.
And if you think about it, that's kind of the moment
that we're in right now.
Is the ability to be fluid in translating concepts
from one domain into another.
I think it works.
But I do think that some of those competencies
are gonna be equally important or more important
as we continue to evolve, as we begin to develop.
They'll be the core skill sets that we've always had,
leadership skills and problem-solving skills
and analytics skills.
But I do think that the ability to translate that
and then also be able to derive from the output, right?
Whether it's from a dashboard or reports or whatever
I think that's also gonna be
really important business skills
for any businessman or woman.
You said right now,
you said like that's the place we are right now.
What do you think has caused that?
What's making that so important right now?
I don't know that I was emphasizing that.
But I think when I look over the past year
and I think about,
we're just on the year mark of COVID for us.
I can remember when we went virtual.
I remember so many of the customer and client calls
that I was helping to fill at the time.
Companies were in a moment, right?
So many merchants were going online.
If they didn't have a huge presence
they needed to create one.
Business models were really shaken.
But everybody was looking for data
to try to help make some decisions, right?
And they needed guidance,
if they didn't have the data, they were looking for data.
If they had the data,
they were looking for guidance on how to interpret it.
If they knew how to interpret it,
they were looking for lookalike information
to make sure they were making the right decisions.
So if you just look at that maturity curve
we have been thrust forward in time.
So I think it would have been happening
maybe in a less compressed way.
I think we've been in a time and compression
where data and digital has mattered.
And so we can't undo that.
And so I think these skillsets matter more and more
just because of the compression we've been under
as a society.
And so we're going to see that
be part of what this next normal or next generation
because I don't really like the word normal.
Next generation, this is now part and parcel
of how we interact with each other.
We will never go back to just being in person.
This kind of connecting digitally
will always now be part of our mandate, right?
And part of the toolbox.
So I just think that there will be more of a need
to be able to interpret and to utilize
than we had a year ago.
That seems like your superpower is taking some situation
whether it's 911 and turning it into a design school.
Or taking a pandemic and turning it
into a more fluid approach to things.
It sounds like your super power in many cases.
Thank you. (chuckles)
I'll take that one.
JoAnne, thank you so much for talking to us.
You've really picked up on a lot of points.
We've covered a lot of ground.
Thanks for taking the time to talk with us today.
Yeah, thank you so much.
You're welcome. Thanks for having me.
(gentle music)
Trevor, that was great.
JoAnne covered a lot today.
What struck you as particularly interesting?
A lot of things struck me in a positive way.
I liked how she talked about design thinking.
She talked about emotional quotient.
And I actually think that's been part of her secret sauce
and part of MasterCard success
with these kinds of initiatives.
Like if you look in the industry,
the CDO role is actually somewhat precarious role.
Because there's very successful CDOs
and there's many CDOs that just don't have that vision
or that ability or that autonomy
or sort of that multi-sided triangle as she talked about.
And so they don't succeed
and they last for a couple of years.
And so I think there's something to be said for that.
What did you think about, Sam
on her comments about implementation the challenges?
Yeah, exactly.
I thought it was interesting that
they've not changed what they're doing
in terms of AI projects.
Because of some of the fact that it's hard
I think they recognize- They've doubled down on it.
That in itself is a little bit of a validation
that what they're doing they believe in.
That even though it's hard they're still continuing.
I think they're picking projects
based on their strategic focus
rather than picking a project to do AI.
That says something that just keeps coming up.
No one's out here saying, "Hey, let's do some AI today.
It's Monday morning, let's do some AI."
These people are trying to deliver
on things that are in their organization's strategy.
And it turns out that in many cases, AI is the tool.
But if it's not, that's okay too. But it often is.
Strategy with AI, not strategy for AI.
Where did I read that?
Yeah, I know, right? Didn't you write it?
(chuckles) That was all David.
The other thing I thought was quite insightful
and she went there very early on is about
trust and ethical use of AI.
And sort of reigning in the AI solution
to make sure it's ethical.
But also using it to uncover hidden bias.
So that you could be more inclusive,
you could be more aware of the workforce
of your customers, of the ecosystem.
What I liked was particularly insightful there
was the need not to view all data as equally neutral.
And that depending on where the data goes to
very harmful things can come out of it.
Which necessitates another layer.
She hinted on it, right?
There's a layer of awareness and governance.
And a mix of tech artifacts as well as process protocols
to make sure that the outputs are ethical and unbiased.
And she talked a lot about the need for mindset change
for that to work.
That people have to ask, what am I using it for?
What is it gonna do?
What could go wrong?
And that constantly probing
for things that can go wrong so that you could preempt it.
But also she saw it as a tool to prevent bias.
We've seen so many stories out there about,
"Oh, this AI system is biased.
That AI system has caused bias."
And I'm not denying those are true.
I'm not a biased denier.
(Trevor chuckles)
There's an opportunity that she saw in that data
to rectify that.
They are a fact.
No amount of retrofitting is gonna take that away.
But she also said we don't ignore the data either.
We just have to know what that provenance is.
I think that's a great point, Sam.
Thanks for joining us today.
Next time Shervin and I will talk with Chris Couch.
Chris is the senior vice president
and chief technology officer at Cooper Standard.
Please join us.
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