#322 How Next-Gen Data Analytics Powers Your AI Strategy with Christina Stathopoulos, Founder at Dare to Data

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Data teams have had to evolve very quickly with everything going on in the AI space. I think the main thing though that that changes is that it raises the stakes for data strategy. The data teams involved, it reshapes what they need to deliver. They might have a bit more pressure now. I think we've seen this big movement happening now for years. Is that the data and the AI teams, they are moving away from being like a cost center to now being like they need to generate and prove their value. What are you doing for the business? How are you generating value for the business? Up until a certain point, you still need the human data analyst or the human data scientist working together with it. We hear a lot of people talking about that AI is going to replace us or even data professionals are saying this job is, is going to be irrelevant in five, 10 years. I don't believe that at all because I'm seeing what it's like to test these, these tools. You use it as like a, like a sidekick. You can't completely rely on. It does make mistakes.

Welcome to Data Framed. This is Richie. In our ongoing quest to figure out a sensible AI strategy, Today we're looking at the role of the data team for data analysts and data scientists, AI agents are upending traditional workflows. I want to find out what works right now and what's still future music for managers. There are big questions about how your data strategy and your AI strategy and your business strategy all fit together. Today's guest is one of the world's top data educators. Christina Staphopoulos is the founder at data consultancy Data. She also teaches both data science and AI strategy across Porto Business School, IE Business School and isdi. Let's hear her thoughts on how AI assisted analytics feeds into AI strategy. Hi, Christina, welcome to the show.

Thanks. I'm excited to be here.

Wonderful. So, just to kick off, I know you've been playing around with AI agents for data analysis and I'm curious, do they work yet?

That's a good, a good question to start off with. I have Been playing around with these AI agent, data analysts, data scientists. One I've been trying out a lot lately is Julius AI. And I think for what it could do, it's amazing. I mean it can program in SQL, in Python. It will walk you through like the steps they're taking to get to the conclusion. Like they don't just give you, you don't ask for an analysis and it just gives you the output. It'll actually take you step by step through what it's doing. It will output all of the code that it's generated to get to there. It even makes like recommendations of like the final, how the final insights can be applied to the business. Because you know, we're not, we're not just crunching numbers. To crunch numbers, you've got to generate some value for the business. So it will even link it to the business at the end how you could use these insights. So I like that a lot. What I've also liked is that you can connect it natively to databases. That's a new thing that I've been trying as well. You can connect it directly to your databases, you don't have to export the data and then re upload it to the tool. So it works very nicely. But I would say that, you know, like up until a certain point you still need the human data analyst or the human data scientist working together with it because I know we hear a lot of people talking about that AI is going to replace us or even data professionals are saying, you know, this job is going to be irrelevant in five, 10 years. I don't believe that at all because I'm seeing what it's like to test these, these tools. You use it as like a, like a sidekick, but you can't completely rely on it because it does make mistakes. And it also doesn't have all the, you know, the business context that you have and the experience that you have. So it's really like you have to combine what you know, your experience, your knowledge together with the tool and, and it can do amazing things, but you still need to be there to validate the output and make sure that it's staying on track.

Okay, I suppose that's reassuring in, well, in several ways. I mean, firstly, it's nice that there are some pain points being solved beyond just like generating some SQL code automatically to crunch the numbers. It's also giving you business insights. It's also reassuring that we're not going to be replaced by robots immediately. I'm curious then as to how your workflow changes so if you are doing data analysis with, with one of these AI assistants, like, does that change your workflow at all?

I think it does, but in a good way. Like I said before, it's like you should treat it like, like a sidekick. Like it's supercharging your workflow. So the way that I'm seeing it changing my workflow is that I've got that sidekick working together with me and it's helping me, it's helping me move along faster. It definitely helps, like with the brainstorming process. It can help with exposure, exploratory data analysis to generate things or to come up with ideas that maybe you didn't initially think about. It can help you move through that analysis much faster than you might do without the tool. And I've seen that it helps me even think outside of the box. Like I mentioned before, the brainstorming part, even when it's drawing conclusions too, it'll help me think outside of the box, it'll help me draw conclusions. But all of this, I'm saying like with a grain of salt, because you still need to be there to validate what it's doing and check it, because they make mistakes. I mean, the tools that I'm using every day that have these AI features that I'm adding into my workflow, they still need to be, you still have to like babysit them because it's like, it's like an assistant, but they're not, they're not a senior. So you need to check what they're doing. But they can help you get to your results, your outcome much faster. I think instead of, you know, needing to double check things that you're doing or the debugging that you have to go through, they can help you get through that much faster. They can help you, the brainstorming and so on. So I think it's more like it just gets added onto your workflow and it helps you move along faster.

Ah, that's interesting. So, I mean, because they're making mistakes, I guess it seems like it might be a really good fit for the stuff you mentioned, like the brainstorming, the exploratory data analysis. Because there you're just trying to ask a lot of questions, get a lot of answers, like really quickly. And if you make mistakes, it's fine, it's only you were going to see them. Whereas if you've got like a final report that you're showing to your boss, the stakes are higher there, or even if you're showing to a customer, then you know the stakes are very high in terms of getting it right. So do you find it, it's more helpful then in the earlier stages rather than the later stages of an analysis?

I think, I mean it's helpful in both. It's right that like towards the end of the analysis when you're going to be presenting to stakeholders like the pressure is on, so you really need to double check it there. But I think it's about even between both of them. It's helpful in both because at least in my case, like I'm, I'm doing the same level of double checking. Like I, I read through all of the work that it generates after it's done, all of the code that it's done, I double check it no matter if it's the beginning or the end, because I found that it makes mistakes in both, in both leases. And you would hate, for example, in like the brainstorming process or the exploratory data analysis to just gives you an output that you really like and you don't really double check it. And then you realize later on that it's made a mistake because then you're going to that mistake, you're going to pull through the entire analysis. It's true that you just don't have that as much pressure maybe at the beginning, but you want to make sure that those big mistakes don't get drawn out till the end. Then you have to go all the way back to the beginning.

Yeah, that's fair. So if it suggests like a really inappropriate analytical technique and you just take it like blindly, like, okay, this is going to work, then your entire analysis is wrong and your whole project time is wasted. So yeah, I can certainly see how there are big problems even at the start of the workflow in terms of the different skills you need. Then if you're doing AI assisted data analysis or AI assisted data science, um, like do you need to learn different things? I mean, I guess proofreading is like a really important skill now. But beyond that, talk me through what, what do you need to learn?

That's a good point. You need good proofreading skills. Now. We didn't need that as much before. As analyst or data scientist, I think that as far as like the skills, I don't, at least in my opinion, I don't think the skills have changed too much. Like all the foundations remain and I think they always will. Nothing has changed in that sense. So it still means that as a data analyst or a data scientist, you still need like the fundamentals, you need Statistics and probability. You need SQL, we mentioned you need data visualization, you need to learn how to program in Python. These types of things, I don't think they, they change. And also you need like this foundational knowledge, which would be like understanding how data works, understanding where it lives, how can you query it, how can you tell a story with it. These are things that you still need to learn so those skills don't change. The only thing is that, like I mentioned earlier, you add on like this new layer of having this sidekick, this assistant right there with you and it's supercharging what you're able to do, so you're able to get to your results faster, you're able to think, it's able to help you push and think outside of the box. So I think that's the thing that has, that has changed. So in that sense, the new skills I guess you would need is to understand like how you can incorporate it into your workflow. Like how can you work side by side with this tool and make sure that it's accelerating your process and not slowing you down. It might take some, it does, I do think there's like a learning curve with it and you need to, you need to practice. It's going to take some time at the beginning, but once you can like get it natively into your workflow, the way that you work, it can accelerate what you're doing, what you're able to do. So really the main, the main new skill is how can you add this into your workflow, how does it work within your, your tool set? And then proofreading like we talked about, like, like you mentioned, that's a really good, a very important skill to have now.

Yeah, it's interesting the idea that, I mean, it's basically like a second colleague. So it's like when a new person joins your team, always takes a bit of time to figure out how are you going to work together efficiently and effectively. And so, yeah, I can certainly imagine there's going to be like, oh, you've got a plan for having extra time just to work out how to, how to make sure you're going to work together. Okay. Okay. So you mentioned data visualization as being one of the important skills data analyst, data scientists need. So it's like a perennial important skill. There was a bit of a scandal at the start of the month, so OpenAI released GPT5, that, this big flashy demo, which I have to say was very, very impressive demo, mixture of use, GPT5. But one of the things Was there was a, an AI generated plot and it was terrible. Caused a minor scandal in data visualization circles. So Gen AI is still kind of bad at creating plots. I'm wondering like how do you do AI assistance for data visualization in a good way?

The whole scandal, I was on top of it. It was kind of crazy how bad the plots were that they generated. So when it comes to data visualization, this is one of my specialties. So I'm very picky about it. And I do not think that AI tools are great at data visualization. We saw that proved by OpenAI. They had GPT5 which is supposed to be so powerful, but it can't even do the most basic of data visualizations. I think they, I think they generate like ugly looking data visualizations. I think they make very basic mistakes. So you need to be very careful if you're using those to visualize data the way that I use them in my workflow, if I'm visualizing data, it's not necessarily to create that final plot as you shouldn't do, because if that's what they did for GPT5, then we see the final results that it gives. So I don't use it necessarily to like create that final plot that I'm going to use. I'm going to present or you know, give to stakeholders. But I will use it like we talked about earlier, like brainstorming, I might, I might use it for the brainstorming process, like to see what suggestions it has for how I should visualize the data and it will give me examples and I can check out those examples. But then I would build my own plot with my own tools and my design specifications with the data. So I wouldn't only rely on it to do that. And then you can also see what interpretations it gives of the data visualization. So the ones that it's generated or the ones that you've created, you can provide it to it and then you can see what, what interpretations does it take from that? Or like what, what business conclusions would it, would it take from this? And you can test that against what conclusions you're making. But again, like we've been talking about up until now, you had to take everything with a grain of salt because it can still make mistakes. I was using one the other day with data visualization and it made a really basic mistake because it was interpreting a graph that it had generated actually and it was saying, it was like saying the opposite of what the chart said, pretty much like it was comparing like male versus female and let's just to make it simple, like comparing male versus female and saying, you know, the males have a higher spending rate than the females. But if you look at the plot, it was actually the opposite. The females had a higher spending rate than the males, but like, I don't know why it interpreted it the opposite way. So if you had taken that blindly and ran with it, you would be making a wrong interpretation. But it just takes, you know, a little bit of experience and just the proofreading to recognize like, hey, no, this, this is wrong. That's completely opposite to what the graph is actually saying here.

Yeah, I mean that sort of stuff is very dangerous because if you don't proofread it, then again when you present to your stakeholders, you're gonna look like an idiot. Because that's a really simple kind of plot. It's like even someone who has just a rudimentary understanding of like how you interpret plots, they're going to spot that. And so, yeah, you have to be very, very careful there. And I agree. I mean I'm the same, like, I get very picky about data visualization, so I'm like, I wouldn't have drawn it like that myself.

So.

Yeah, in general, do you have any tips on great data visualization? Always happy to hear that sort of thing.

Yeah, I mean I could give, I could go on and on about tiffs. I'm very involved when it comes to data visualization. I even, I'm, I'm an adjunct professor at a business school at IE Business School in Spain and I teach a course there on data fluency where we focus on the last mile of analytics, which is data visualization, data storytelling. And I also lead that course across the business school. So I'm, I'm always coming up with like new materials, new frameworks when it comes to data, viz, I love it. But I think a couple of like very important tips for everyone and I think AI should be listening to this and applying these tips as well.

Episode of Data Frame gets scraped by the OpenAI and anthropic bots. Yep.

I can only, I can only hope. I can only hope. So I think the, the number one tip that I always give is that simple is best and less is more. So you don't need to over complicate things. You don't need to make these overly exaggerated or trying to like put too much like prettiness into the graph when you have to remember like, what's the purpose of this graph? What are you trying to show? And focus on that and do it in the simplest way possible so that your target audience can understand, they can get the message immediately. Another thing is to always design with your audience in mind, which kind of connects back to that. But just keeping your audience top of mind that you're not building the data visualization for yourself, you're building it for someone else to read it, to get whatever message you want to, you want to transmit to them. So always design with your audience in mind. If they are a more advanced audience, you know, maybe if they are data practitioners, they're data engineers, then they have more technical knowledge and maybe they can, they can understand a more elaborate, a more complex data visualization. But if you're talking to like business stakeholders, directors, not technical people, then try to stick with those more basic graphs, what they know so that you don't confuse them and so they can get the message quickly. And then if I can add just one more tip, it would be that it's not just about choosing the right graphs, that's important, choosing the right graphs for your data, but it's also about choosing the right colors. I see colors are overlooked a lot or the importance of them, and they're incredibly important. So I think just having, I guess reviewing some of these basic things that we learn in kindergarten, but color theory and understand how you can use those effectively in your data visualizations. I wish I could like show the screen right now. I have it on my phone. But no, because right before we were recording this, I was watching the news here in Spain. I had my husband like pause the TV because they were presenting like this map and it was like, you know, those typical color coded maps and it had like a legend on the side. It had made really big mistakes with the colors. And I was like, pause this for a second. I need to do a picture of this data visualization. My husband's probably like, what is wrong with you? Everything has to be like data focused. But I took a picture of it because I was like, this is a perfect example of them not thinking about the colors and not even thinking about like these simple design things. And we see this all around us all the time. Like I just said, this was on the news, you see it on websites, business, you know, reports. It's really simple things that people, it's just because they don't take a step back and, and consider those foundational, those foundational elements that you need for a data visualization.

Absolutely. I mean, the co thing's incredibly important. Like I'm colorblind and I've had a lot of cases where looking at a plot and people like, oh, yeah, look at the difference between those two, the red bar and the green bar. I'm like, those are the same color. I don't understand.

My, my husband is colorblind too. So I, I, I teach about this in my classes about colorblindness. Have you heard about the Beardis color palette?

Veridus?

Yeah, Ms. Viridis.

Yeah, yeah, go on. Do you want to explain what's Viridis?

Oh, yeah. Viridis is a, it's a color palette optimized for colorblindness. So I think it's one thing that I mentioned, like in my courses, my trainings, is that if you find that you have, you know, colorblind people on your team or a client who's colorblind, be careful because you might be presenting a plot where they can't really tell the difference between, you know, certain categories. So I recommend using this Beardus color palette, which is optimized for colorblindness. So I'm sure you, maybe you're, you must be. If you're colorblind and you work in the data field, then you know it for sure.

I have come across it, I've definitely used that. So I can actually see what's going on in, in the plots I create. So, yeah, very useful. But I've been wondering whether I've been pronouncing it wrong all the time. You pronounce it Viridis.

I don't know how, I don't know how to pronounce it, to be honest.

We're not sure. It's some Latin name. Make it up as you go along.

Yeah.

All right, so we're talking about how generative AI isn't good for creating data visualizations. One thing it sort of does work now for is generating SQL, at least most of the time. So do you want to talk through, like, how you go about doing number crunching differently when you are using AI assistants?

Yeah. So I think it's similar to what I mentioned for the AI agents earlier. For typical data analysis, data science, it's still, you're still treating it like a sidekick to help you with those SQL queries, right? I think that it's, it's very helpful, especially like when you know what you want to do and it can, like, help you format that SQL query, especially when you get into, like, more complex ones or you forgot you know how a certain function works. It can help you generate that and create that for you. You still always need to validate it, of Course, make sure that it's, it's going along correctly and it's generating what you wanted and so it's not like you're, you're never like off the hook with it, but it does help you again, it's helping you just move along faster and helping you, when you forget you know the exact syntax for something or how to, for how to do some sort of formula. And then I was going to add that. Another, another thing that I found it really helpful for when it comes to SQL is like, it's amazing at doing it the other way around from what I just explained. So to, to give you like an example, I use this tool called Sherlock, which is like a repository for SQL queries. It's very similar to something that we had at Google, I was at Google before, and we use something called Plex. And it reminds me of this, this environment that we had of Plex, but it's called Sherlock. And so it helps, it's like a repository for saving SQL code and it has like these embedded AI features. So one of them that I found that's quite useful, especially for a team where everybody is generating and sharing their, their SQL code base, is that when you write, you, you write out your SQL code, your query and you save it, it will then automatically describe what it's doing in natural language and it will like save that with the code. So if someone on your team accesses it, they can read through that description and understand what it's doing before they dive into the code itself. So like give this just an English natural language explanation. Somebody even like a business user would be able to read it and be like, oh, that's what this code is doing. Or somebody on the team, they can read it really quickly or the manager and get a quick idea of what that code is doing before you actually, you know, have to analyze the code itself. So I found that also really useful. Interesting. Yeah.

So going from I've got some SQL to I've got a description of it. I mean, there's so many use cases of this because I mean, first of all, nobody likes documenting their own code. It's tedious and boring and yeah, definitely better outsource to a machine, at least if it gets the answer right. But then also you've got a prompt then to reuse next time you want to write some SQL. That's a bit like that because, yeah, you got the natural language explanation of what you want to do, throw that into your generative AI and then, yeah, that's the prompt for next Time.

Yeah, that's a really good point. Exactly. By the way, is there anything else you would add to this? I mean, are you, I know you're a data practitioner too, so are you using AI in any other way when it comes to like SQL or data viz and your analytics workflow?

Absolutely, yeah. I mean, it took a while to kind of change my approach because a lot of the time I was like, okay, I would start off writing code, then if I made a mistake, I wanted to like, ask AI to fix it. But now a lot of the times I'm like, I'm trying to force myself more to start by prompting and getting the AI to generate code and seeing how well it works. So I'm trying to, I'm trying to be AI first and go, okay, let's get the AI to do everything and see where it breaks, rather than trying to write the code myself and then, and then get the AI to fix it. So I've been trying to change my workflows. I'm getting old. It's harder to change workflows, I think, than it was 20 years ago. But yeah, I, I am trying to use AI a lot more for data analysis and data science stuff.

And I would, I would say that, you know, sticking with those old ways has its benefits too, because if you rely too much on it, you can forget how to do that. Like you said, you want to, you know, you want to just start with it from the beginning, which is good too. But I, I like to go like back and forth because sometimes I like to challenge myself to do it. So I don't forget because that's, I think that's one risk that a lot of us data practitioners are going to have is that you become like over reliant on it, that you're just, you know, generating the natural language. You're not coding it from the beginning. And then what if you get like rusty? Then it gets harder to validate it. So I think that's something that like all of us are going to have to figure out is like, how much AI first is okay. Because if we start to forget that fundamental knowledge, we can't even write the code without that AI tool, then maybe we're becoming like, you don't want to get too reliant on your assistant if you can't do your job. So I think, I don't know how we can manage this, but we'll have to find a better, a better balance so we don't, we don't all forget what we're doing.

Yeah, That's a definite risk. Oh, I saw a study, it was on Polish doctors or something recently. So they were given AI assistance for doing diagnoses for a few months and then they tested their performance afterwards without the AI. And basically everyone had been using the AI, their performance decreased, they got worse than they were at the start at diagnosing conditions. So yeah, there is a different problem where you become reliant on AI and you become unable to function yourself. I mean, I guess it's probably like relatively straightforward. If you don't have AI for a while, you will relearn skills a lot faster. But yeah, it's a certain, certainly a worry that your skills will degrade over time.

Yeah, I think so.

All right, so we've talked a lot about what happens on an individual level. As a data analyst or data scientist. I know you also do a lot of strategic work. So let's talk about what happens at the sort of team level, the executive level. Do you think data strategy has to change now that we have AI assistance?

I think data, like data strategy, data teams have had to evolve very quickly with everything going on in the AI space. I think the main thing though that changes is that like it raises the stakes for data strategy. So the data teams involved, it reshapes what they need to deliver. They might have a bit more pressure now. I think we've seen this big movement happening now for years, is that the data and the AI teams, analytics teams, whatever it may be, they are moving away from being like a cost center to now being like they need to generate and prove their value. Like you really need to show what, what are you doing for the business, how are you generating value for the business? And I think it's putting more pressure on data teams to now. It's like they've always needed to generate like accurate data that's important for the business and now it goes like beyond that. They need to make this data that's ready for the AI systems. So it needs to be like machine readable, it needs to be labeled, it needs to be model ready, whatever it may be. But they need to make that data now ready for whatever AI systems the company is using. And then I also think that like the data team scope is shifting a lot too. Depends on the company, the exact organizations. But now they might need to have like a wider scope where they need to manage like real time data, they need to manage more unstructured data. Whereas before, you know, it was very like structured database focused. But now you need to go beyond that. You might need to be preparing, you know, more unstructured data that requires all sorts of new skills, real time data, et cetera. So I think it's just been like this shift and more pressure on the data teams when it comes to everything happening in the AI space.

I feel like this consistent story with like every team in every organization. Oh yeah, there's an increasing amount of pressure. You've got to take on more new tasks and you got to figure it out now and deliver more value. But yeah, so it's a problem that's pretty universal. But have you seen how like the organizational changes? So because you got a data strategy, you've got an AI strategy, you got a business strategy, how do you keep these things aligned?

Yeah, you got to keep them aligned because they need to work. They got to work like hand in hand. I think you mentioned three things. Right. So you mentioned business strategy, data strategy, and you have AI strategy. Right.

I mean there's probably even more, but.

Yeah, then there's more strategy. Yeah, BI strategy. We can like keep going on and just adding on little pieces, but I think it's like they need to work hand in hand and you also need to have very clear like the roles, responsibilities of each one, who's responsible for each thing, the teams, and understand what exactly they're responsible for. So I think the business strategy should be pretty clear because that hasn't really changed. So the business strategy is like the why and the what. So you're defining the vision, the goals, identifying the value creation opportunities, those sorts of things. Then you have the data strategy, which again, like the data strategy itself hasn't changed so much. That's still the foundations, that's the enablement. Right. They're the ones responsible for like the data infrastructure, the governance, the quality, the accessibility, those types of things. That's data strategy. And then you have on, you add on now like the AI strategy. So that's more of like I guess the how. Right. So defining how you're going to use the data and the tools at your disposal to deliver those business outcomes, you need to link back to the business strategy that I talked about first. How are you going to deliver on these, these goals, the vision of the business using the data that you have on hand, the tools that you have and you're going to apply AI to all of it. So I think like the, they all connect together and they need to work hand in hand. And when it even comes to like the roles, the people working there, there should be, you know, people dedicated to each type of strategy. But you're also going to have a lot of, like, overlap too, where people kind of are responsible for multiple things. You'll have a lot of, like, data and AI professionals who kind of work on both sides. It's very hard to separate the data from the AI. Right. So you'll have a lot of these overlapping responsibilities, roles between the strategies too.

Yeah, that's interesting and I like the idea. Well, actually, business strategy is probably not changing that much. The way you make money, you still can make money that way. So, yeah, it's mostly the AI strategy that's kind of new on there. But you talk about shifting roles. Do you want to talk me through, like, how data roles are and responsibilities are changing then? I mean, you mentioned you got to find ways to get more value. How do you make this change?

Yeah, I mean, I think like, like we've said it's about, like, value creation. You're expected now. You're not just a cost center. Like, you're like, off the hook. You need to really be able to prove how you can deliver for the business. And I think this is tough because a lot of data people, myself included, I can get like, very wrapped up in. I love, like, solving problems, but, like, in the code and the data, I get very, like, hands down in the project that I'm doing. And you get very disconnected from the business itself. This is risky for you, like, for your career. Because in the end, if you want to, if you want to grow in your career as a data or AI professional, no matter what, you still need to prove to the business, like, what value you bring to the business. And now more than ever, we talked about it, like, the pressure is growing. So now more than ever, you've got to be able to prove what value you're delivering. What, what exactly, you know, the work that you're doing. How is this helping the business? I don't think this has actually changed because this has always been that way, but I think it's become even more, like, important. Even more fundamental to data and AI professionals is you don't want to just get pushed into, like, a back office. If you want to grow in your role, you need to be able to prove how you're delivering for the business. And I think a way to, like, work on this then is never forgetting, like, what's the end goal? The end goal is not to write this nice SQL query that the rest of your team can understand or whatever. It's not, it's not this type of. That's not the end goal. The End goal is like understanding what is this going to do for the business? Like how are you going to use this and apply this in your actual business to generate more sales or save costs or whatever it may be. So I think it's always like, it's like a change of mindset or at least like an evolving mindset where you cannot forget, like what's the end goal? You've got to be, you've got to be focused on that. I don't know if that, if that makes sense or if you could, if you would add on anything else to it. But I think that's like the biggest thing that I've seen and something that a lot of us struggle with. I think a lot of data practitioners struggle with it, myself included. Like I, like I mentioned.

Yeah, absolutely. Definitely something I've fallen over in the past in my career. It was like, oh yeah, like just going for that perfect model, that perfect analysis and then actually project deadlines come way past and no one cares anymore. You sort of missed the opportunity to do something useful for your business. So, yeah, I agree, agree that keeping the end goal in mind is very important. One thing I will say though, sometimes it is just very satisfying to, you know, write some good code, do some, do a good analysis. And if you're purely focused on, let's grind out an analysis as fast as you can to get a result, sometimes you don't leave it in good state for if you want to reuse the components of that project later on. So there are definite trade offs between like short term goals and long term goals that sometimes get forgotten about in the, in the chaos of business.

That's true. That's a very good point. Yeah.

All right, so I know one other thing you are passionate about is trustworthy AI. Now I've heard of responsible AI. So is trustworthy AI. Is that the same thing or something different?

No, I think they're different. So I would say that responsible AI is like, it's like an umbrella term. So it includes everything. It includes like ethical, safe, sustainable AI. And by doing all of that, you generate trustworthy AI. So I think that's kind of. You create the trustworthy AI by creating responsible AI. So responsible AI comes first umbrella term, and then trustworthy AI would fall underneath that.

Okay, all right, so subset of responsible AI. Cool. So yeah, talk me through the relationship between data and then trust in AI.

I think the link between data and trust in AI, I mean, it's fundamental. So the AI learns and it makes decisions from the data. Right. And if you're working with bad data, whether it's like biased or it's, you know, full of errors, or it's incomplete data, for example. And then you're going to generate unreliable AI and you're going to lose trust. So the trust in your AI, it all comes back to the data. Actually most things that happen with AI, it comes, it comes back to the data, but the trust, very fundamental. If you want to be able to trust the AI, you better have good data.

Absolutely. I mean, I think with all the hype around AI, people often forget that data is still incredibly important. It's kind of central. If you want good AI, you've got to have good data. Let's talk about what can go wrong. I love a good disaster story. So yeah, what are the sort of common mistakes you make if you're trying to create trustworthy AI?

There's a lot of mistakes that you can make. So I think I'll give like a couple of examples. So one common mistake is like black box decisions. So you're not transparent about the rationale behind your decision making or you simply cannot explain how the AI is, is arriving to its conclusions. So it's these black box algorithms. Right? This is common, I mean this is common all, all over the place. But if we give an example like a bank, so let's say like a bank is using an algorithm to make decisions on who they're going to give a loan to or a mortgage to and then if you get denied and the bank can explain why exactly you got denied, then you're going to create like this distrust and this frustration with your customers. Right. So I think black box, black box algorithms, black box AI, that's a really common mistake. Another thing is this is one I love, I could go like down a rabbit hole into, but it's biased or unfair outcomes. So that's a really common mistake as well. To give an example. So there's this really big lawsuit that got opened quite recently in the US against, I'm not going to say names, but against a well known SaaS company, a software, as a service company. And the lawsuit is alleging that they were using the, this AI driven process. AI driven, like applicant screening for people applying for a job there and that it was discriminating against people over 40 years old and it was preventing them from being hired. And 40 is not even, it's not even that old, but it was preventing people.

I'm right there with you. 40 is not old.

I don't even, I can't even believe it shows that, that age. But okay, so supposedly people over 40 were not being hired because the AI was, was discriminating against them. It was biased, it was biased AI. And then so that's one thing bias another one as well that I'm seeing a lot especially like recently is over promising on AI capabilities. And you probably, you've probably seen this a lot too. So you like, you have companies that are like marketing that their AI is 100% accurate, for example, which is practically impossible. Or you have like, or they're marketing that like they have these fully autonomous systems when the truth is actually very far from it. Like they're, they're not, they're setting these very unrealistic expectations. So when, when people find out you're going to, you're going to have problems if you're over, over promising. And then if I could add one more mistake, but this links to like what we were, we were talking about before. It's just the mistake of having poor data quality. It always comes back to the data. So we always say, you know, garbage in, garbage out. I think that remains true. It's always going to remain true. If you train your AI or even like your basic analytics, your basic business intelligence systems, if you train it with outdated, inaccurate data, then you're gonna, you're gonna produce bad results, unreliable results. So another mistake is just relying on bad quality data.

Okay. Many different things can go wrong then certainly I can see, okay, yeah, your model or your AI is not explainable. That's going to give you problems if you, particularly if you're in a regulated industry, big no, no in finance for sure. And then yeah, certainly data quality, it's a well known problem that yeah, you've got bad data quality, you're not going to get anything good out of it. Presumably we've just scared people now about how none other AI is going to be trustworthy. Do you have any advice for people who are now terrified, like, okay, how do you go about like making sure that any work you do, any AI you are making use of, that's going to be trustworthy?

Yeah, I don't want to scare people. There's good and bad. I mean, we don't have time to get into it. But there's like the other positive side of responsible AI, Right. Because I even teach courses on the subject of responsible AI, so I love it. There is a lot of focus on the negative side because there's a lot of risks and a lot of risks that people are not aware of and people are becoming more aware of thankfully, but it's taken some time. There's the other side too. So I don't want to, like, be focused just in the negative. But there is a good side to responsible AI. Like, for example, I talk about in my classes the good side of responsible AI, like, using it for very good purposes. So AI for good or data for good things. Like, for example, using AI to help people, like with disabilities. So, you know, like the blind or deaf people, there are incredible applications of how they're using AI now to help, for example, deaf people, especially deaf children. I found out through my research that deaf children have a much higher, much higher illiteracy rate. So they don't learn to read and they're using AI to tackle this. There's a problem with teaching them how to read because they can't hear. So the parents oftentimes don't know how to teach them to read and they fall behind. They're using AI to tackle this. There's other applications for blind. So there's these incredible applications of like, good, good responsible AI. So I know this is getting a little bit off track, but I did want to highlight the good side. So you had asked about, like, what can we do to make this, this trustworthy AI, Right. I think there's a lot of different things you can do, and it depends on the problem, because if we're talking about, like, bad data or if we're talking about biased data or black box algorithms, like, each thing has specific things you can do to tackle it. There's different, you know, frameworks, there's different ways you can tackle it. Like when it comes to bias, for example, you have these fairness metrics where, metrics where you can literally, like, measure the fairness of your, of your systems and try to improve on that fairness with black box algorithms as well. There, there are, there are techniques for explainable AI, or xai, explainable AI to break down these black boxes and create glass boxes, is what I say. So it depends on, like, what exactly you're trying to tackle. There are ways to go about it, and there's lots of work being done. Just because the responsible AI space, I think, is exploding. It's growing a lot because companies have started to realize, like, it's not just about jumping on, onto the, the AI wave, because if you jump too fast, you can, you can create some serious risks. And if you do it more calmly and you do it in a responsible way, you can avoid those risks, especially in the long term. You might get ahead in the short term, but you can face some Serious risks in the long term. And what I always say is that the company should tackle this proactively, because if you tackle it proactively, you avoid what I call the trifecta of risk, which is reputational risk, regulatory risk and operational risk. If you put responsible AI into the strategy from the start, you can avoid, like I said, the reputational risk, regulatory risk and operational risk.

Oh, man. I think we've gone for like the pep talk back to scaring people again. But okay, yeah, so talk me through. Do you want to talk me through the three things in more detail? So reputational, what was it? Regulatory and operational risk. Talk me through. Yeah, what's the difference? And what are these things?

So reputational, I think is pretty, is pretty obvious. That's more like, like exactly the company that I mentioned earlier, the SaaS company that's going through a lawsuit because they are, you know, discriminating against people over 40. I'm not saying the name here, but if you Google it, you can find what company it is. This is going to hurt their reputation because now they're getting called out for discriminatory practices. And you see other, you know, there's been other controversies where companies have been found to be like, discriminating based on race or based on gender. And you don't want something like that next to your name as a company. I mean, that's horrible for the reputation of the company. So that's, that's for reputational risk. I think that's pretty obvious for regulatory risk as well. That's, that comes back to, you know, there's changing regulations now. Um, it's not. So it's not like you can just do everything as you want blindly. There are certain regulations depending on the region, the place in the world that you are. Europe, for example, is a bit more strict. It's stricter than, than other. The European Union is stricter than other, other places, other regions in the world. But I think every, every, every region is starting to develop their own data and AI regulations. So that's the regulatory risk, like being able to be prepared and develop things in accordance with that. And then operational risk as well. Operational is just problems that go wrong operational wise, and it can come back to responsible practices. So not having like, even like connecting back to bias, if you're, if you're marketing like a product that is supposed to be for the whole population, let's say, but you're biased against, you know, males or biased against females, you're missing out on half of the population. Right. And so it can, it can change you operational wise that you are not able to deliver on what you're doing, even trying to do from the start. So hopefully that, that makes sense. Between the three. I could talk and talk forever about responsible AI and the risks, but again, there are good things too. There's, there's a good side too.

Absolutely. And I think just being aware of all these risks is a good start towards like being able to deal with those risks and mitigate those risks. But that, that hiring example you gave, I mean it's interesting because AI hiring tools, there's a lot of them about now, but yeah, it's one of those things in the eu. AI, I think all these sort of HR use cases, they're classified as high risk just because there are these problems with bias and discrimination. So yeah, definitely something to cautiously approach rather than dive headlong into. All right, so since you mentioned like AI for good, I do love that, like, certainly like it's cool like you're doing some data science project to like optimize ad clicks or something, but like actually helping people with real problems. That's a, that's a really wonderful use case. Have you done any more work in the space or do you know any more sort of good examples of AI for good? We need a cheery story to finish up, I think.

Yeah, yeah, let's, let's finish like on a, let's finish on a more positive note. So AI for good. Yeah, I love to explore this in my, in my course on responsible AI. So I already mentioned like the disabilities one. I think that, I think there's so much possibility for AI in the healthcare space, medicine, but particularly helping people with, with any sort of disability experience the world, be a part of the world in a, in a better way. So I mentioned the deaf example. They're helping kids that are born deaf because they have a very low literacy rate. Blind. There are a couple of products out now that are like glasses for the blind that help them, that help describe like their surroundings, help them read text. Like for example, these are things that a lot of us, if you're not blind or you're not deaf, you don't think about the problems that somebody with these disabilities would, would face on a regular day to day basis. So if you take somebody who's blind that goes to the supermarket and maybe they have their guide dog with them, but what if they want to buy, you know, milk and they can't read the, the COVID of the milk, they can't tell what, which one is whole milk, which One is skim milk. Well, you have these glasses now that can help read the text out loud to the blind person so they can understand what they're looking for. Even the, the glasses have, like, embedded into them, you know, hands free, so they can call Be My Eyes. I think it is. There's a couple apps like Be My Eyes and other ones where you can live, call somebody as a blind person to help you navigate a situation. They can, they can see with you. So that's for deaf blind. Another really interesting example for AI for good. I wish I could remember the name of it, but I don't remember right now. It's an AI company that is helping detect wildfires early. So right now I live in the south of Spain, and Spain is experiencing these massive wildfires. I mean, it's, it's just killing. It's. It's burning acres and acres all over Spain right now. They're trying to get it under control. But there is this company that does like early detection wildfires. And because they, I can't remember the exact statistics, but they said that just most wildfires, they don't get. They don't get found or detected early enough. When somebody finally notices it, they call. Then you have to get people dispatched out there. You lose a lot of time, and in that time the wildfire is spreading. So it's these systems that they have. I can't remember if it's through drones or maybe even like the towers as well, the electrical towers, but they're watching around and looking for early signs of a possible wildfire, like little smoke going up. And they automatically report this to the local authorities and they're able to go out there and check it immediately and so they can stop it from spreading and getting out of control. So I think that's another useful, useful use case.

Oh, all these examples are wonderful. I mean, certainly, like, I like the sort of the, the idea of using technology to help with your sort of sensory defects. I mean, I wear glasses. I've got my regular earphones at the moment. Usually I wear hearing aids as well. But yeah. So technology to help with sensory things. Brilliant. I also like the idea of wildfire detection. I love backpacking. And yeah, I like to not burn to death in the wilderness. So that's a really good use case. It's nice when data and AI are sort of solving these societal problems.

Right.

Okay, so just to wrap up, I always love to find new people to follow. Is there anyone whose work or research you're interested in at the moment?

Yeah, there's lots of people. But I think one person who I've been following for quite a long time now and I love her work, I recommend it for everybody to follow is Dr. Joy Buolamwini. So she's based out of MIT in the U.S. she works a lot in responsible AI. No surprise there, probably that I'm following somebody closely in responsible AI. But she's done a lot of work, especially on when it comes to bias, algorithmic bias, discrimination. She does a lot of championing or campaigning for more diversity in the teams that are working on these systems, as well as the data that we're using to train these systems. So I highly recommend people follow her. She has a fairly new book out as well called Unmasking AI. So I recommend that all about her work and I think her website is called poet of code poetofcode.com so you can learn more about her, follow her on LinkedIn. That's, that's, that would be like my number one recommendation for others to follow.

That's one of the. So Joyce actually previous data frame guest as well. So, yeah, she came on the show talking about unmasking AI. So, yes, definitely recommend checking out her work. Wonderful. All right, thank you so much for your time, Christina. Great chat.

Of course. Thank you for having me.