 Hello and welcome to my career in data pockets where we discuss with industry leaders and experts how they have built their careers. I'm your host Shannon Kemp and today we're talking to Katrina Ingram from Ethically Aligned AI. More and more companies are considering investing in data literacy education, but still have questions about its value, purpose and how to get the ball rolling. Introducing the newest monthly webinar series from DataVersity, Elevating Enterprise Data Literacy, where we discuss the landscape of data literacy and answer your burning questions. Learn more about this new series and register for free at DataVersity.net. Hello and welcome. My name is Shannon Kemp and I'm the Chief Digital Officer at DataVersity and this is My Career in Data, a DataVersity Talks podcast dedicated to learning from those who have careers in data management to understand how they got there and to be talking with people who help make those careers a little bit easier. To keep up to date in the latest in data management education, go to DataVersity.net forward slash subscribe. Today we are joined by Katrina Ingram the founder and CEO of Ethically Aligned AI and normally this is where a podcast host would read a short bio the guest but in this podcast, your bio is what we're here to talk about. Katrina, hello and welcome. Hey Shannon, how are you? Good. How are you doing? I am doing well. Looking forward to our chat. Me too. So let's dive in. I love your background. You are the founder and CEO of Ethically Aligned AI. So tell me about the company. Yeah, absolutely. So there's a little bit of backstory to this company. I actually did a midlife career pivot in 2017. So I went back to school and I started doing a master's degree in communications and technology at the University of Alberta. And while I was there, I encountered this topic of AI ethics. So this is 2018 and I was really interested in this topic. I just wanted to dig into it. I spent all my time researching it and trying to understand how data scientists and machine learning experts were thinking about ethics, and so when I graduated in 2020, I really wanted to keep going in this field, but I didn't really see any companies really focused on this issue. So I decided to launch my own company in 2021, Ethically Aligned AI, or Social Enterprise. And we focus on helping organizations to build and deploy better, more responsible AI solutions. And what that's looked like in the past couple of years is a lot of education because people don't really know how AI has been impacting them. I think they're starting to get a sense of that now as we're kind of living through this, you know, AI hype cycle at the moment. But a couple of years ago, it wasn't really apparent. So I did a lot of work in education, a lot of training, a lot of workshops and a lot of consulting. And I'm really excited because we're embarking on some new work in the realm of tools and some technologies that we're going to build to deliver a process to help with responsible AI. So that's a little bit about me and the company and how we got started. Oh, that's very cool. And I'm excited to learn how you decided to make that, make her shift. But let's back it up as so a little bit. So tell me what you think, what is your definition and then a responsible AI? What's the, what's the focus there? Yeah, it's really interesting. So you can look at that from two different perspectives. So the perspective that first hit me was, look at all the harmful things that were happening with AI. So this is 2018, we're talking about facial recognition, we're talking about AI gone wrong scenarios, we're talking about all the ways in which AI was impacting people and causing really harmful outcomes, discrimination, bias, unfairness of all kinds. So that's kind of one way of framing it. Or you could look at it and go, what are all the ways in which we could do better and be more responsible and more thoughtful in terms of how we're building these products so we can get to better outcomes. And I do look at it both ways. Sometimes it's helpful just to illustrate the ways in which harms are happening so that you can get to that better outcome. So that's really what responsible AI is all about the practices, the processes, the tools that you need to get to these better outcome. Oh, very nice. So, then, how do you work with your customers? What's your typical work week look like? Well, we are a startup. So every week is a little bit different. And I still have one foot in academia. So for example, this week I was marching a lot of papers. I taught a summer course on AI ethics at the university. So I was doing a lot of that. But on any given week, I'm doing some business development. I'm talking with new potential clients. I'm doing consulting work with existing clients. I'm putting together training courses. I spend a lot of time reading because this deal is, it's just happening so rapidly. Everything is changing and influx. So I really feel the need to be really up on the latest research and the latest things for going on. So I do a lot of reading. And then I do some media engagements, things like this podcast, for example. So it's a nice mix. It's a whole bunch of different things that I'll do in a week. Sure, sure. Absolutely. So, so let's tell me then, Katrina, was this the dream? So like say you were six years old and was this the dream? When I grow up, I want to be a founder and CEO of a company that promotes responsible AI. No, no, we're close. What was the dream? I think it could be a fashion designer. That was kind of my first dream. Yeah, yeah. And then the adults, they kind of come in and they're like, are you sure you really want to do that? And so I didn't pursue that actually wound up in business school instead. Studying to be a marketer. So it was kind of like, you know, this reality check on the dream. But I think in hindsight, I probably gave up on that a little too soon. Yeah, the other thing I wanted to do is write. I really wanted to be a writer. And I kind of feel like I'm closer to that because I'm doing a lot of writing. I do writing for my blog. I write my courses. I write speeches. So I'm maybe like closer to that dream. But yeah, the idea of AI, I mean, that was just not even in the, you know, in the consideration set at all. Oh, interesting. So you went into study business then. So what, why did you pursue that? And where did that lead you? What did you start? What did you end up majoring in? Yeah, I'll tell you the journey that you get here. It's really weird. I think probably a lot of people think they have a really like circuitous career journey. I know I did. I know that's true. So yeah, so I started in business school and to get my creative fix, I thought I'll major in marketing. That'll be my thing. So I started out when I graduated. I went to work at an ad agency. So this is in the 90s. And I didn't love it. It was a little cut for a little Melrose place. If you remember that TV show, people were kind of like that. That wasn't me. I couldn't step on other people or do any of that. So anyways, I found my way into the technology space, working at a company called Crystal Decisions. And that might be a name that's familiar to some of the audience because we made Crystal Report, which is the big reporting software of the time anyways. We were in the BI space and I was doing marketing and some of it was really interesting, but honestly, it wasn't quite creative enough. I didn't quite feel like it was my thing. And on my way to work every day, I used to walk past the Canadian Broadcasting Corporation building, the CBC. And I thought, you know, wouldn't it be fun to work in broadcasting? It'd be so cool. One day I quit my job and I applied at the CBC and I started working in radio. And I loved it. It was great. And then that led to other career opportunities to manage a radio station here in Alberta where I'm from. So I managed an indie music station called DKU Way Radio. And it was super fun. And that's kind of where I stayed until that 2017 moment where I decided I was going to pivot and go back to school. So in a weird way, like I'm back in technology again, but with a completely different slant on everything. Sure. So fascinating. How fun to work for, you know, radio. I've always envisioned and some kind of radio career in some other dimension past life of mine. This is a fun place, especially in radio. It's kind of like that old TV show WKRP where you have all these weird characters and you're just trying to, like, get them, you know, organize. It kind of felt like it was hurting tops on any given day. I'm sure. So, you know, so you went back into technology, you know, and so when you started school again, you know, what, what was the intent there? What was the the, the really the drive, the passion that you started of classes you started signing up for? Yeah, well, to be totally honest, Shannon, I was, you know, I was feeling a bit lost at that point because what was going on for me is I was watching media blow up in some ways. So a lot of the challenges with media, which have to do with technology because technology came in and it kind of undercut the business model for a lot of media organizations. And when you see that, especially with news, printed newspapers being kind of the biggest example. And while I was a little bit insulated from that, I had a lot of friends working in media, and I really had this moment where I thought, do I want to keep going in this field or do I want to try some new things and I honestly didn't know what those new things were. So this degree sounded interesting and I thought I'll go and work it out and figure out what I want to do next. It wasn't super intentional in the sense of I want to go and retrain to be acts. It was a little bit more hazzard and accidental and just kind of like, I'm going to try this, I'm going to see what emerges from this process. And, and good things did emerge from it. So, so that was really how it all worked out. How brave. I love that. That is so brave and bold and just way to follow your your instincts and doing what you need to do for you and take a chance. So, I love that. Our bus catalog of courses offered on demand and industry leading live online sessions throughout the year. The Dataversity Training Center is your launchpad for career success. Browse the complete catalog at training.dataversity.net and use code dbtalks for 20% off your purchase. So what was your biggest lesson then so far in in your career. Yeah, I think this idea we're told at a young age that we're going to have this really linear career path like we're going to go into something we're going to climb the ladder we're going to get you that you know the top of that ladder. And it's all going to be super linear and so my biggest lesson has been it's it's not really like that and that's okay. In fact, I recently I love this quote I love Malcolm Gladwell and he said this quote he said it's a risk not to change careers and I'm like that's fantastic. I feel really validated. Yeah, something like that. So that's kind of like my big you know maybe life lesson and then the other thing I'll add like as an entrepreneur the other really big piece of advice that I got is to say yes to opportunities. You may not always know exactly how you're going to accomplish that goal, but there's something about stepping out there and saying yes and then figuring it out as you go that I think it's really essential to the journey of being an entrepreneur and being bold about things and so that's something that I think about a lot too because sometimes it feels like risky like I don't know how I'm going to do this but you know what I'm going to sign up and say yes and I feel confident I can figure it out. So true so great, you know, part of why we started this podcast is because you know I too grew up, you know, believing that I might that career path would be so linear. And, especially in data, you know, there for for anybody older than Gen Z, I think, you know, it wasn't a was there wasn't really a, you know, a college degree there wasn't a you know, data was just this kind of obscure thing out on the edges. So nobody in data management really had a straight path to it is everyone just kind of stumbled into it. You know, and it's so true and it's so fun to explore and hear how people have explored to find their, their path and their niche in data. Yeah, absolutely. I'm even just thinking about your the title data versity and I'm thinking about diversity and data and all these coming together. I love that. Yeah, it's funny is, in fact, when I'm doing a webinar, you know, people turn on the transcript which is AI, you know, based right to, and then the transcript never gets data versity right that always translates to diversity. You know, progress. So, so tell me, so now what's your definition of data. Yeah. I'm going to answer that question I'm going to try something here we're going to do a little experiment if you're up for Shannon. Oh yes. Okay, here's the experiment. So everyone listening and those of us here on the podcast. We all have heart beats. I hope we all have heart beats because we don't have it with bigger problems here. So, my question is, is your heartbeat data was just like pause for a second and consider that is your heartbeat data. So we think about that. We can think about that the idea that a heartbeat has the potential to be data. And I don't know Shannon are you wearing Fitbit at all. I have my Apple watch. Yeah, so you are probably rendering something your heart perhaps into data you're turning it into data and those of you out there listening to have a Fitbit or an Apple watch you're doing the same thing. So we have data as this thing that we can capture. So there's lots of different things that can be represented so I always think of data as a representation of a phenomenon it's kind of a snapshot of something. And it's linked to this idea of being able to capture something. And what's happening right now is we have more and more ways of capturing things so we have more and more ways of turning things into data that we could never really turn into data before and we have ways to store those things and we have ways to leverage those things into new, you know, analytics and so forth. And so that's part of how I think of data. And the other piece I think of is this idea of data as an assemblage so it's not just the output it's not just like the heartbeat number for me it's that whole process of what we've done so we captured it. We're using it in certain ways we've made decisions to even collect it as data. And so what I do is I look at all that context and I look at the ethical questions that might come with that. So that's kind of a way of how I think about data and kind of what it means to me as a data episode. Oh, I love that perspective. And it's so true. And I do, I do love my fitness data. I do. We were, I was at a conference recently, and there was a yoga class offered and the gal next to me, I'm like, oh I forgot to start my, my watch. And she's like, oh yeah me too she's like it doesn't count unless we capture the data. So I'm like it's a huge orange theory. And so people are like, well what do you mean like aren't you the one who's all about privacy and ethics and all of that and I am but I'm like, but it's useful to me because I you know I can see what I'm doing in terms of my work out and where I'm going and so it's kind of serving me in that moment. And so yeah, I'm big on the health data as well. I love it. That's the fun thing about data right with in everything and everywhere and, and we get to work with it and so many exciting ways that benefit us. Yeah. So tell me Katrina do you see the importance then of data management and the number of jobs working with data increasing or decreasing over the next 10 years and why. It's so vital. You know, when we think about data data is everywhere and every company is becoming a technology company so you should think of tech companies like Microsoft or Apple and yeah those are tech companies. But increasingly what we're seeing is your grocery store is becoming a tech company, the company is becoming a tech company it's all about data data is really the heart of all of that. Data is everywhere and data is part of everyone's job, whether you recognize that or not. And so this idea of really understanding like being data literate understanding data governance understanding data management like it's incredibly important understanding ethics as well, all like super important to pretty much any kind of job that you're going to have now and in the future. So what advice would you give to people who are looking to get into a career and data. Yeah, there's so much to learn about data. I mean it's such a fascinating topic. Obviously there's great podcasts like this and all of the great resources on dataversities so I highly recommend all of that you've got some fantastic and real heavy hitters on when I looked at who's who's who of data Laura Sebastian Coleman Peter Akin they've all been on the podcast so there's really great people to follow. I also love the game of community so game as the data management community, they have this massive book, which is their body of knowledge, and I spent some really good time with my local data group, going through that book. I think it's really fun doing it as a group project and you're like helping each other out and that and also building community at the same time so I think get connected to community that would be part of my advice. And then I think also sometimes it can feel intimidating if you're especially if you're thinking oh this is a curve pivot or something different, but there's ways to add data to whatever you're doing right now. If you're an educator, even education background like myself, you can help to learn about data and teach data courses if you're a lawyer, maybe you can pivot into privacy and data, like there's ways to kind of add data to a career that you already have if that's what you're doing. So there's lots of different opportunities. I love that advice. I love that so and Katrina, I'm curious. I'm so curious to like what's the number one, like advice isn't just that you give to your customers about being responsible with AI. You know what is the the core of being ethical. Yeah, responsible. I mean really ethics. A lot of ethics is about being really thoughtful and intentional about your process. So I think number one, if you're wanting to be responsible that's a fantastic start you're starting out you want to do something positive. I think you can look at everything holistically. So sometimes it's really tempting just to look at the solution, but you should look at the problem that you're trying to solve before you kind of jump to the solution. And sort of figure out what's going on here. And from an ethics standpoint what makes sense as a solution who might be impacted so start really thinking holistically about how you're approaching things. And then just try and iterate and do better next time I think that you know sometimes you'll get intimidated and they think I have to have everything perfect the first time out. I try to encourage people but that's not necessarily the case. What it's about is trying to build up a capacity for ethical thinking and responsible AI in your company, and then just kind of building on that and iterating so that's really what I encourage people to do. Very nice. And if somebody wanted to reach out to you and solicit your services, how would they find you. Sure. Well, there's lots of information on my website, ethically aligned ai.com. I'm also on LinkedIn a lot so you can find me there. It's another good place. Yeah, those are probably the two main sources. This has been so great. Katrina, thank you so much for joining us today. Well thanks so much for the insight it's been a lot of fun. It's been a great fun. So I'm really excited to watch what you do and because it's, I think this topic is just getting bigger and bigger and better and more important. It's so important right now with all the generative AI and everything else out there. So thank you for taking this on. Well, and to all of our listeners out there, if you'd like to keep up to date in the latest in podcasts and in the latest in data management education, you may go to dataversity.net for subscribe until next time and stay curious everyone. .