Innovating for Inclusion – Ep.7

Embracing Diversity- Ep.6
November 4, 2020
Changing The Game – Ep.8
November 18, 2020

CEO and cofounder of Deep Labs, Scott Edington explains artificial intelligence, its impacts related to the consumer experience, and how this technology is used to enhance inclusion.

Melyssa Barrett:  Welcome to The Jali Podcast. I’m your host, Melyssa Barrett. This podcast is for those who are interested in the conversation around diversity, inclusion, and equity. Each week I’ll be interviewing a guest who has something special to share or is actively part of building solutions in this space. Let’s get started.

Dr. Scott Edington is the CEO and co-founder for Deep Labs. His career spans over two decades of creating next generation technology capabilities in the payments, defense, and intelligence sectors. Prior to co-founding Deep Labs, Dr. Eddington served as the Global Head of Research and Development for Visa Incorporated. Edington played a critical role in both the formation and execution of Visa’s innovation and technology strategies, and served as a member of Visa’s architectural leadership board. Prior to joining Visa, Edington was a technology executive at Booz Allen Hamilton, where he ran the firm’s Technology Injection (emerging technologies) practice. In this capacity, he was responsible for all of Booz Allen’s technology injection, R&D, strategic prototyping, and knowledge management and information sharing efforts. Edington also directed the firm’s external technology relationships with Academia, National labs, the vendor community, and various technology incubators. He holds a degree from the University of Virginia, Johns Hopkins University, and industry certifications in the areas of security, strategy, and network computing.

Please join me in welcoming Dr. Scott Edington. Dr. Edington, thank you so much for joining me for this wonderful conversation. I am ecstatic to talk to you about all of the things that you have accomplished with respect to, now, you being the CEO and co-founder of Deep Labs. Maybe we can just start there and you can talk about, what is Deep Labs?

Dr. Scott Edington:  Sure. Well, number one, thanks for taking the time to spend with me this afternoon. I really appreciate the opportunity. Deep Labs, in a 30 second overview, million foot view, it’s a company that’s pretty much focused entirely on context-aware artificial intelligence capabilities. What that means for the non-geek out there effectively is, think of a world where computer systems really fully appreciate what persona you might be exhibiting at any given moment in time.

The easy example I always use is, Scott Edington sitting in Oakland, California pre-pandemic is a little different than Scott Edington having shelter-in-place for two and a half, three months in Oakland, California. Yes, from an identity perspective, it’s still Scott. But obviously, the context and everything around all of us has shifted. Because of that, having systems that are intelligent enough to understand that, “Yeah, it’s still Scott. But, the personas that Scott will now exhibit will be dramatically different or would have shifted based on context, based on surroundings, based on everything that’s happening in 2020.

Melyssa Barrett:  Yeah. That’s interesting that you call them personas, because as in the world of diversity and inclusion, and of course I’ve worked in the area of identity, it’s all about your identity. It’s almost like we have multiple identities all over the place, whether you’re at work or at home, or what your shopping looks like, or any of that kind of stuff. So, those personas I’m assuming can really engage a person in different ways, depending on what they’re trying to do.

Dr. Scott Edington:  I think you nailed it, Melyssa. Obviously, given your background, you fully appreciate all the interesting things that have happened in the industry in the last 10, 20, now 30 years of our respective careers. The reality is, is that circa 2005, 2010, simply using some static attributes like mom’s maiden name or social security number, that was fine, frankly. That’s how most of our systems sort of keyed off of understanding, “Is it really Scott, or is it not?” But as we all know, unfortunately, due to some well-known data breaches and other things that occurred have now in the world, those static signals are frankly, very much antiquated or in my vernacular, decayed. They’ve decayed because everyone has access to them now and you can Google where I went to high school. You can Google where I was born. It’s freely available, and most times, pretty accurate. If the things you can’t Google, you can certainly find on the dark web for next to nothing.

Because of that, as we both know as practitioners, there were a number of points [inaudible 00:04:58] that came up specifically focused around things like added verification, or device signals and device intelligence. Which again, were absolutely fantastic, absolutely fantastic as a way to patch the holes. But unfortunately like anything else, the advanced persistent threats that exist out there, whether it be from nation state actors, whether it be from folks who are individuals trying to do nefarious things, these signals have also decayed. What we’ve really been focused on is figuring out a mechanism and capabilities that we have to effectively create a full context-rich understanding about an actor. An actor for us, by the way, could be an organization, it could be a consumer, it could be a human being, it could be a bot.

But the idea is effectively, we want to be able to understand at multilayers, which signal should be used to identify what context that actor happens to be in, and then understand exactly what persona we’d expect that actor to exhibit at this moment in time, versus what the reality is. Then, effectively figure out a way, a mechanism to see whether or not, is that really who we expect Scott Edington’s persona to be right now? Versus, “You know what? We’ve actually seen his persona before, and the last time we saw it, it was associated with a bad act. You know what? I know they presented all the correct credentials, but decline, decline, decline.” Or, “You know what? It’s a little different than we might expect from Scott at this moment. But you know, oh, he’s in Washington, DC today as opposed to Oakland, California, therefore let’s not introduce friction to this consumer experience. Let’s go ahead and approve that transaction.” So, it does work both ways either from a propensity standpoint or an authorization standpoint, all the way to the opposite spectrum, which is advanced risk capabilities.

Melyssa Barrett:  Yeah. We have truly geeked out right now. I know I have my mom sitting here going, “What are they talking about?” No. Tell me a little bit about how you got here, because the stuff that you’re talking about when you start talking about artificial intelligence and all of the things that occur in the background to try and detect bad actors. There’s a lot of folks that I know, even from a representation standpoint, there are not a lot of people of color in the industry that you’re in. So, how did you start out, and how did you actually get to where you are today?

Dr. Scott Edington:  Yeah. Not to sound too cheesy about it, but I think it really starts at home. I was blessed to have, and still do, two parents who are educators. Part of that, their parents, both sets, literally were also teachers. So, education is always very, very important for us in the house. Now, if you ask my parents thought that and whether or not I’d value that as a grade school kid or high school kid, might get a different response, but it was always certainly very important in our household. My older brother Charles, now an electrical engineer, also went to University of Virginia undergrad, and ultimately ended up going to Booz Allen and working in the intelligence community, was also very much focused on signals and noise. So, I’ve always played in that realm or at least, certainly been interested in signals and what does that really mean, even in layman’s terms or non-geek terms, and that understanding of how signals can be harnessed.

It’s always important to me and interesting to me. Even as I went as an undergrad at the University of Virginia, my focus was always trying to understand why people do what they do when they do it. If you ever look at my undergrad curriculum, for lack of a better term, it’s a combination of economics, computer science, philosophy, government, and even religious studies. The idea was, what are the attitudinal, what are the psychographic, what are the economic things that were incentives to influence what people do and why.

As I was leaving undergrad down at University of Virginia down in Charlottesville, most of the firms that were there were hiring for either New York or Washington, DC, and most of the offers I received were actually for Washington, DC. As you might imagine, being around the Beltway, a lot of those folks were really focused on public sector work and specifically, my focus was on defense and the intel community. As you might imagine, signals and noise are very important. Not to totally date myself but when I was coming out of school, that was circa late ’90s, early 2000s, and there were obviously some things happening around the world that necessitated having folks who really understood how to parse signals, how to use that to create a competitive advantage whether it be on the commercial side or for war fighters and the intel community as well.

Melyssa Barrett:  Interesting. Wow. So, you just jumped right into defense. Then you ended up at Booz Allen from there or…

Dr. Scott Edington:  No. So, yeah-

Melyssa Barrett:  Or, that was part of what Booz Allen did?

Dr. Scott Edington:  Exactly right. Our focus there at the time was around primarily the defense and intel community. Frankly, honestly I kind of lucked into the role that I had, which was it wasn’t dedicated to a single customer or single client. It was actually across the whole spectrum. So, I had the opportunity to, you can’t make this stuff up, literally meet folks like the Secretary of Defense and whether it be the people literally on the ground about to go Iraq or later on Afghanistan, all the way to the folks who happened to operate in some of the other circles that you’d expect in the intel community. 22-year-old kid who was born in Greensboro, grew up in Philadelphia area, you’re not going to expect to see things like that.

Melyssa Barrett:  Yeah, no doubt. That had to be kind of a shift for sure. I’m assuming then you had some mentors, sponsors, people that kind of helped you, because I can’t imagine going from school to like, “Hey, by the way, here’s the Secretary of Defense and we’ve got folks going to Iraq or…” What was that like?

Dr. Scott Edington:  It’s one of those things where I think sometimes, youth is actually, the old adage, it’s wasted on the young. At the time it was like, “Well, yeah, this is normal, right? Yeah, of course you meet with a two star general and you brief them on the cool stuff you’re doing. That’s what everyone does, right?” When you look back 10 plus years later and you’re like, “No. That’s not what happens, Scott. Come on, man.” So, to answer your direct question, I was very fortunate early on. My first executive boss at Booz Allen… Actually again, looking back, how has this happened? Mike was very instrumental in my career. He was a former fighter pilot, again, he flew F-4 Phantoms in the Vietnam War, literally ran R&D for McDonald Douglas back when that company happened to exist. You hear these things, you’re like, “Oh, like Top Gun?” In my head, I’m like, “Oh, just like Top Gun, the movie, right?”

He’s like, “Oh, actually no, literally it’s like that.” Talking about landing aircraft on aircraft carriers out in the Indian Ocean, it’s crazy stuff like that. I share that story only to say is that particular gentlemen took, looking back, a fair amount of time with me and probably was far more patient than he probably should have been with me. I think that’s important because number one, it taught me how to be a true professional, but I think number two also, the notion of learning to pay it forward. Again, I’m telling myself on a little bit, vividly remember a conversation I had when I was 22 or 23. I think it was about a year and a half in my career at Booz. Apparently, I thought I was doing a great job.

Well, he let me know a little different in the sense that… I remember, it’s like it was yesterday. He sat me down and he said, “Scott, I have this problem and I’m hoping you can help me out with it.” I was like, “Yeah, of course.” Because, I know everything. “Of course I can help you out.” He says, “I have this really bright kid that I think has a lot of potential, but he’s not getting it.” I was like, “Well, I’m happy to mentor him. I’m happy to…” He’s like, “Scott, come on man. It’s you. Wake up, it’s you. Here’s what you’re not doing, here’s what you need to do. Here’s the expected outcomes,” blah, blah, blah, blah, blah. At the time, I was a little bit hurt, a little taken aback. But I was just like, the fact that he took that much time, the senior executive at this Fortune 500 company, best consulting firms literally in the world, that must mean something. It must mean that there is something to be had here in terms of what he believes I can do and thus, I should be able to do.

I’ve always sort of really went out of my way to find… sort of want to say diamonds in the rough, but diamonds in the rough where it’s like, you know what, sometimes people just need a truthful conversation. A real truthful conversation, and it’s not that there’s lack of intent or any malice sometimes when people aren’t performing, it’s because sometimes they just need a chance. Sometimes they need maybe some straight talk, and other cases that you just need to be very clear in terms of what the expectations are, which is not always given. Frankly, it’s not always given to us. 20 plus years later, I vividly remember that like it was yesterday, like I mentioned before. But, also go out of my way to just talk with some of the young folks, especially people of color, about that story because frankly, he didn’t have to do that.

Melyssa Barrett: Yeah.

Dr. Scott Edington: Looking back, it was a five minute investment at this time, but it’s been now a 20 plus year career that just definitely benefited from it.

Melyssa Barrett:  Yeah. That’s phenomenal. Those people, you think back and they have a pivotal impact on your career, I’m sure, for you to be able to recall it like it was yesterday. But, I think those are the things that continue to push you to levels of excellence. My father used to always tell me, he used to always compare me and the president to like, “What did you do today? Because you’re slacking.” You always want to have high standards for what you’re doing.

Flipping back to the work that you’re doing in artificial intelligence and signals. One of the things that I hear a lot about is… Just in terms of people of color, and I spend a lot of time obviously on financial education. There is this whole segment about financial inclusion. Essentially, there are many people, specifically of color, who are not part of the financial system, but I think all economies think that they should be working or at least have a strategic plan to address financial inclusion. Can you talk a little bit about how artificial intelligence and where we’re going impacts people that are not… Maybe they don’t have a bank account, or maybe they’re using check cashing or doing some alternative financing. How does that impact some of the stuff that you’re doing with artificial intelligence and all of those discussions we’re having on privacy and other things?

Dr. Scott Edington: Yeah. Well, number one, thanks for the question. It’s a topic that I’m, as you know, very passionate about. Specifically the underbanked populations, whether it be here in the US or certainly globally. The numbers are very clear. They disproportionately affect people of color. So, I would say first and foremost, I think the key thing is understanding… I think I’m going back to my college days. What are the influences to why that’s happening? So, that could be both macro and micro. I think there are, obviously, systematic reasons for some of the underbanked reasons.

But, I also think from a techie perspective for a second, if you sort of take a step back and look at how typical bank systems and micro services systems work, into how they onboard new customers or clients, how they make credit underwriting decisions, how they verify things like income or verify addresses. These are all sort of things that, frankly, you and I take for granted given where we are, but these are real things that are hurdles and introduce friction even in something simply as, “I want to start a bank account for my eight year old.” Or, “I want to start a college fund.” Or, “You know what? I just want to have a checking account so I can write checks.” Or, [inaudible 00:17:30] you write checks now, but have some sort of stake in the digital economy.

What I would say, going back to my earlier statement around signals and noise, the ability to not always just focus in all through the antiquated signals of yesteryear, mom’s maiden name, what high school did you go to, the old school static signals, but more focus on the near or recent signals that now exist based off of where we are and normally, where we would be in 2020 and hopefully now in 2021. I stress that only because whether we, again, talk about digital onboarding remotely, because now I guess, everyone really doesn’t want to go into a branch. But, how do you actually identify that is in fact Scott without having a social security card? I’m dating myself. Social security card or a passport? How do you do that remotely? How do you do that securely? How do you do that in a frictionless environment that’s not overly invasive, number one, but still has a confidence level that’s high enough that actually meets the guidelines necessary for a risk-based system.

A long answer to your very short question, that the reality is is that for the underbanked populations, keying off of things that are in static signals of yesteryear is actually a method of disfranchising. It is. If we’re able to use the modern signals, if we’re able to use the context we [inaudible 00:18:58] around personas, then in fact you can start providing banking services and banking capabilities to those who ordinarily wouldn’t necessarily have a generic checking account. If you’re talking about, let’s say, the prepaid market as an example, because as we both know, that’s something the underbanked population does use, does leverage. How do you actually still enable them to be onboarded and still meet the demands of like, the Patriot Act? As you know, that sometimes is difficult for some people from a hurdle perspective. So again, is it simply keying off of the status signals? Or can you actually start replacing some of those static signals with dynamic signals that we now enjoy? The answer, overwhelmingly, is yes.

Melyssa Barrett:  Well, and it’s interesting because it seems like the more signals we get, the more models are built that provide things like credit scoring and fraud scoring capabilities. If the underbanked population has a lack of information in those models, then it can create other challenges for whatever the objective is. Which, to your point, hopefully is less systemic in terms of kind of hurdles around systemic racism and stuff like that in the past, because at least it’s using information in an objective manner. But if the data is not there, then I guess you have other issues. So, how do you account for things like signals that aren’t really there?

Dr. Scott Edington:  Mm-hmm (affirmative). No. So, I’m just going to agree with you and I would answer in two parts. I think first thing is, let’s just fully recognize how the old systems work and what they over-index for. Which is, given your background you know firsthand, they’re over-indexed for things that frankly, tend to represent certain populations, not others.

If you just look at it just from a pure data science and modeling perspective, what does the sample look like? Figuring out what should the sample be in 2020 versus maybe what it was in 1985 or 1990 when a lot of these systems were first sort of theorized, right? If you sort of eliminate, dare I say, the lack of proper sampling and you actually fix that, that’s step one. Step two is if you now have a proper sample or a full representative sample, then you can start looking at other methods and other attributes and characteristics that could represent the gap that you were just mentioning. Right? So again, as opposed to keying off of where you went to, I’m making this up, where you went to high school. The fact that you’ve had a job for the last two years at the same place, frankly, I’d rather know that.

Melyssa Barrett:  Than where you went to high school.

Dr. Scott Edington:  Yeah, where Scott was in high school back in the early ’90s. Right? I say that somewhat flippantly, but it’s actually true. Again, if you really just think about the context surrounding each individual, human beings are remarkably great at picking up on things. You know when someone’s a little bit off that day or your gut says something’s not quite right. Up until now, those models, they didn’t really take into account instinct or gut, they were simply just hard, fast numbers. “This is what we’ve always done, it’s declined.” As opposed to, “Wait a minute. This guy’s had a job for the last two years, same place, same income level, that company’s doing well. I can tell you that company doing well, because a thousand other factors that we have access to. You know what? He’s actually not a credit risk. That’s actually someone we want to get behind.” As opposed to, “We had to decline because we can’t figure out where he went to high school.” Who cares?

Melyssa Barrett:  Yeah. Yeah. No, it’s really interesting. What do you think we’re going now, since you’re all into the artificial intelligence and cryptocurrency and all of that? What does life look like 10 years from now?

Dr. Scott Edington:  I’m glad you said 10 years as opposed to next year.

Melyssa Barrett:  It’s anybody’s guess, 2021. Right?

Dr. Scott Edington:  I’ll just be happy to make it to 2021. Seriously, I think from a financial services perspective, I think it really does come down to just hyper-personalization, but not in a creepy way where it’s like, “Okay, yeah. Uh-uh (negative). No. This is too sci-fi-ish, like 1980s sci-fi movie-ish. I don’t want that.” But what customers do we want, and to be fair, it’s also a little bit generational. Right? So, I’m old now. The kids that are coming up right now, they have a certain expectation about what an experience looks like versus someone, let’s say, my parents’ age who are like, “Wait, no. Actually, I want to actually have to type in my name. I don’t want them to know it was me that just walked in the door.” It is slightly generational.

I would say the answer to your direct question, 10 years now, it really is all about hyper-personalization. It is about understanding not only that, “It’s Scott, but it’s Scott on a Friday afternoon, someone dressed down. Right now I can tell you I can extrapolate that Scott’s sort of quasi-business Scott, but he’s sliding into the weekend. So, his persona will have shifted. The things that Scott will expect to have as he walks into a restaurant or walks into a grocery store will be dramatically different than if he was Tuesday afternoon, full suit in New York City, walking down the street. Yes, it’s still Scott, but hey, I’m going to influence Scott to maybe purchase this item. Or, you know what? Don’t bother Scott at all, because very clearly, he has a serious look on his face, he’s carrying his briefcase and he headed to a customer meeting.”

Again for me, it is about total hyper-personalization. It’s about understanding exactly what persona that actor is exhibiting at that moment in time. What persona we expect that person to exhibit in the window of time. Then ultimately, how do you best create an environment that is friction-free and also very secure, that again creates that hyper-personal experience such that that consumer wants to use your product or that consumer wants to be part of your community. Not to totally geek out on you, but that’s what’s coming in. Frankly, that’s what’s already here.

Melyssa Barrett:  Well, that’s really interesting because, I’m maybe dating myself now, but I still remember the Jetsons when I was growing up. That’s what it feels like. We’re kind of moving into the Jetsons, which is pretty old when you think about what they were envisioning way back then. So, very interesting stuff.

Dr. Scott Edington:  Well, I’ll tell you. What’s funny, though, about the… Because I used to watch the Jetsons [inaudible 00:25:37] and all that stuff, too, is that if you really dissect those shows, all that stuff’s already happened. So, whether it be the scooters that try to knock me over in Oakland half the time, or down here in DC. That’s real, right? Whether it be all the sensor technology that enables things like the Segway devices to work or whether it be even our online experiences where lo and behold, I’m talking to you via video camera and real-time feed, it’s almost like you’re in the same room with me. That was all… I don’t know about you, but I was going up in the late ’70s and the 80s. That was all sci-fi stuff. I didn’t think that was going to happen. That’s here now. Even all the way down to using a mobile device and being able to unlock it with your face or your fingerprints, or to be able to transact [inaudible 00:26:30] in some currency halfway around the world, and as you know, Visa, less than a second. That’s amazing, and that’s already here right now.

Melyssa Barrett:  Yeah. I love it. I love it. I’m going to kind of go off on a little bit of a tangent because I know now that you lead a company, Deep Labs, there has to be lots of discussion on representation and diversity. I know you have somebody that’s dedicated to, I think they call it People and Culture, if I remember her title properly. I have been at places where people say, “I don’t want to talk about company culture. We don’t have a corporate culture. What does that mean?” Now, we have this whole segment of people that are focused on diversity and inclusion. I think probably every practitioner would agree that ideally those jobs should go away at some point, because really, we should have diversity and inclusion, belonging, equity all embedded into all of the company activities. Are there things, especially in your business I imagine, with representation being challenging for STEM students. What are some things that you guys are thinking about in terms of accessing talent in those areas?

Dr. Scott Edington:  Yeah, great question. As you know well, that’s a topic I’m quite passionate about and fully believe in. I would say my mindset, to be fair, is also very much… Given the fact that both my parents were children of the ’60s growing up in the South. Again, my grandparents both sides were educators. So again, education’s very important to make building blocks of how you get ahead, so to speak. That’s how you achieve the corporate American training. With that as a backdrop, and my father worked at HBCs for most of his career. Again, understanding how HBCs work, understanding how the network effect associated with that, whether it be when I was with Booz or at Visa, certainly now, we do tap into from a pipeline perspective.

The good news is, there’s plenty of folks out there who are super motivated, highly intelligent, and they just want an opportunity. The difficult part is figuring out how to map those folks who have all those attributes that you want into roles that, and frankly also locations, where there are communities of people of color. For us at Deep Labs the cool thing… I’m just going to plug my company for a second. Sorry, I have to do it. Is that we have locations, whether it be in the Bay area or places like New York and Washington, DC, which as you know, have large communities of people of color. We are actively, actively seeking folks want to join a company that is very much forward and forward looking, forward thinking, but also very much mindful of very clear core values around things like accountability, which is important, but collaboration and integrity and having high degree of teamwork.

I stress that because at the end of the day from a pipeline perspective, especially within the STEM perspective, most folks who come up in those programs, they’ve had to abide by those core values just to make it. You have to be accountable not only to yourself, but to other classmates. You have to have integrity of your work. Obviously you’re not going to get your diploma if you’re not being honest. You have to be able to show teamwork and collaboration because oftentimes, you’re dealing with group projects and everything else.

For us, again, really having a dedicated function within our company focused on diversity, inclusion, and people and culture we believe is a fundamental enabler to the success of our business. I would say, having spoken with other people of color that either have co-founded or founded other companies or senior executives of Fortune 100 companies, that is something that I believe is universally shared at this point. I will say that, unfortunately for us, due much like everyone else to the pandemic, it hurt a little bit our ability to recruit if for no other reason, just getting in front of people. But, I would say for those who are listening that are actively seeking positions, there are people like me who are actively looking for people like you.

Melyssa Barrett:  Yeah. That’s awesome, because even during the pandemic, I think people thought that there’s no jobs out there and I kept trying to send them out, because every other day somebody would say they’re hiring even through the pandemic. I think it’s interesting to see how growth occurs because clearly there’s some contraction going on, but then in other areas there is still lots of growth happening. I know we’ve seen lots of digitization growth over the last several months. So, it’ll be interesting to see how things develop. I’m looking forward to seeing you in 10 years flying a self-driving car somewhere. Or, maybe back in defense somewhere. Who knows?

Dr. Scott Edington:  Who knows?

Melyssa Barrett:  Well, thank you so much for being with me today and it’s always a pleasure catching up with you, Dr. Edington. I know you do a lot of teaching. I know that’s near and dear to your heart as well, so I appreciate you bringing up historically black colleges and universities, because a lot of the HBCs don’t get the credibility, the visibility that they really deserve. So, kudos to your parents and your grandparents for putting so much into you that you can continue to feed it into other generations.

Dr. Scott Edington:  I appreciate that, and I will definitely pass that along to them. They’ll be pleased to hear that.

Melyssa Barrett:  Awesome. Well, thanks so much for being here, Scott, and I look forward to staying in touch.

Dr. Scott Edington:  Thank you, Melyssa. Take care.

Melyssa Barrett:  Thanks for joining me on The Jali Podcast. Please subscribe so you won’t miss an episode. See you next week.