The Equitable Algorithm: Can AI Empower the Underrepresented and End Discrimination?

 🎙️ ABOUT THIS EPISODE

 The future of AI isn’t just about smarter algorithms—it's about building systems that actively reduce discrimination and foster equity. Pius Bozumbil, founder of True Talent and AI enthusiast, shares how you can harness the power of AI to level the playing field for underrepresented communities, from democratizing wealth strategies to creating truly inclusive products.

You’ll discover how AI can be a personal empowerment tool—how it helps individuals grow wealth, develop tech skills, and even build start-ups against all odds. Pius shares practical examples: transforming investments, coding with AI, and tackling bias head-on by designing algorithms that recognize and counteract their own prejudices. His insights reveal that bias in AI stems from data—biased data that can be remediated through intentional design, transparency, and continuous governance. We break down the core risks: how biased training data can reinforce harmful stereotypes, and the critical importance of diverse teams in developing equitable AI. Pius emphasizes that real inclusion requires empathy, diverse perspectives, and a commitment to ongoing review—especially as tools scale and evolve. Leaders will learn the key questions they should ask to assess whether their AI solutions promote fairness or perpetuate bias, and how governance structures must adapt over time.

This episode is vital for anyone building or deploying AI—whether in recruitment, finance, or societal systems. It’s a roadmap for transforming AI from a reflection of human prejudice into a force for meritocracy and social good. Prepare to rethink what’s possible when technology is aligned with human values, and learn how to lead with intention in the age of AI. Perfect for executives, entrepreneurs, and technologists committed to ethical innovation. If you believe AI’s greatest potential lies in empowering all of us—this conversation will inspire you to make it happen.

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Website: Trutalent - The Talent Intelligence for Deep-Tech Hiring

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Transcript

SPEAKER_00 0:00

And the represented community need to get crazy obsessed about AI. It's the most powerful platform that they can use to level up. The biggest risk for me is that inherently it's been trained by bias data. Data that perpetuates perpetuates old narratives that are wrong. Okay. Perpetuate assumptions that are fundamentally wrong.

SPEAKER_01 0:31

For a leader listening right now who's building or deploying an AI system, what is one question they should ask their team to test whether their product is likely to reduce bias or amplify it? So see this as the litmus test for equitable AI. In a world struggling with systemic bias, many hope that artificial intelligence can be a tool to create a more equitable future. But what if it makes things worse? Welcome to the Anaya Algorithm, the podcast for leaders who want to build the future intentionally. I'm your host, Magdalene Anagashti. Today we are tackling one of the most important questions of our time with our guest, Pius Boswinville. This is a conversation that moves from the personal to the universal, from one man's journey or story of using AI to level up to the immense challenge of building AI that can reduce or even end discrimination. This is a deep, challenging, and ultimately hopeful look at how we can build a more equitable algorithm. Please join me in welcoming Pius Bosum Girl. You've proposed a topic that is not only technologically relevant, but profoundly human. Welcome to the Anaya algorithm.

SPEAKER_00 2:22

Thank you so much. Great to be here.

SPEAKER_01 2:24

Awesome, Paius. Would you mind starting by introducing yourself?

SPEAKER_00 2:28

Yeah, so um I'm Pios Bosombil, a founder of um a startup called True Talent. We're trying to use intelligence to help talent teams to find exceptional people. And with this, it started off basically from my own personal pain of trying to find talent where you spend a lot of time in manual, druggery, intensive manual work. And I thought there was a better way to do it. So I set forth to create true talent to do that. I'm also a massive AI enthusiast. I remember when ChatGPT first came out, I was one of the first to um sign up for it. And ever since I've been experimenting with AI to test its limits, how I can use it in my personal life and also my professional life. Uh so I'm really looking forward to having a very, very fruitful conversation on AI.

SPEAKER_01 3:14

Awesome. And I'm excited to have you on this session, and I'm looking forward to an extremely educative, inspiring, and insightful session. Paiot, you wanted to start with a personal story, which I think is the most powerful place to begin. He said you wanted to share how you use AI to level up. Can you tell us about your journey and the specific ways AI has been a tool for your own empowerment?

SPEAKER_00 3:43

So I remember the first day I tested it, I ran to my wife, you know, a lot on ChatGPT, started playing around with it, writing emails. I remember sort of like putting on different personas, as you call them, the prompt engineering. And I realized the power of how you can actually use it to address some of your weaknesses, right? So, for example, one of the things that I love, and one of the reasons why the use case for me using it on an everyday basis is how it constructs your communication, breaks things down, explains things, demystify things for you. And so for me, I found very, very practical ways. And also like the most powerful way that I've used it on a personal level has been um I've become very obsessed with investments. Uh, I'm looking at ways to build wealth, ways to increase income coming in, how can I double it? How can I increase it at the very minimum? And I've been wondering how the ultra-high net worth people invest their money, um, how they grow it uh to a level where you know a sort of their wealth just keeps increasing, right? And so I started this prompt experiment where I asked uh Chat GPT to pretend to be my private banker for a very respected investment bank that we all know. They're one of the best. Um I don't want to um you know mention their name, but essentially able to prompt a private banker. I was able to go into my trading account. Um, you know, I use an app for all my investments. I took a screenshot of all the investments that I had and then fed it to the AI. And then I asked the AI to pretend as if it was trying to grow my money from where it was to an ultra-high net worth individual. I was very, very impressed with how the AI was able to analyze my investments. I gave it a very specific task to grow the money at a minimum of 100% like return year on year. Um, and I intentionally said that at a very ambitious sort of target. I was watching the AI do some market analysis. It was able to take some of the stock that I had. And at the time I wasn't even using my um uh the the free ISA that government gives you. The first 20k that you invest is tax-free. I wasn't even using that. And then the AI was able to pick on that immediately. And I was able to reorganize investments, suggested some of the shares that I wanted, asked me about my sort of like investment persona or you know, how I like to invest. Am I a risky person? Am I and I just went the full throttle that look, I would rather you know you go aggressive and find some stocks. And it was it went, found stocks, and I remember at the time I was very desperate to get into SpaceX, um, OpenAI. I wanted to like get investments in those in those avenues. And then it came back and said those types of investments are only available through a private channel, but it was able to comment platforms and so on and so forth. And I looked at this tool, I was like, look at this. How could I have afforded a private banker? There is no way, you know, income that I have or or the you know the category of wealth that I have. There's no way I can afford, you know, a private banker. They end more than me to start with. But here we are, a tool has been invented, and I'm able to create something for myself. And this is very, very practical. I see the benefits, the recommendational stocks that he gave me, they're actually making me returns. They are, you know, I'm not losing any money as a stance, right? Uh making more than the SP 500 uh with only investments. So for me, it just paints a picture of the future that awaits us.

SPEAKER_02 7:34

Right?

SPEAKER_00 7:35

Be able to immerse yourself in this tool, how powerful it becomes on a personal level, and also be able to complement you as an individual. So wherever you're lacking, this could be a strategy of like really addressing all the gaps that you have personally. Uh so for me, you know, AI, I've only got good things to say about AI, and I'm not naive to the risk and so on, which I know we're gonna be covering.

SPEAKER_01 8:02

Wow, this is so inspiring. Thank you for sharing that, Pius. It's a powerful testament to the idea that technology can be a democratizing force, right? Because you've literally identified a better approach to addressing a limitation that um you otherwise you couldn't have been able to overcome. How do you see your personal experience scaling and what are the biggest opportunities you see for AI to empower other underrepresented individuals or communities, whether it's in education, access to capital or career growth?

SPEAKER_00 8:43

So for me, the first thing I'll say is underrepresented people need to obsess about AI. I truly feel that it's the biggest platform that it can use to level up. I've never seen a transformational tool in my lifetime that actually gives you a tool that can impact your life in a very direct and tangible way. So for me, and and let me let me give you this example so it sort of puts things in context. I was in the middle of fundraising for true talent. And as I was fundraising, I started to ponder. You know, at the time I had a team, you know, my MVP, I had to, you know, give the MVP to someone who developed the code and it came back and it was very, very buggy, right? You know, the curious nature of me, I started asking questions. I'm like, I've been using AI for investing, and you know, um, it's successfully, you know, acting as my private banker. And I said to myself, and I did my research, and I found out that one of the number one use cases for AI is actually coding or engineering, software engineering.

SPEAKER_02 9:47

Yeah.

SPEAKER_00 9:48

And so um I started experimenting with it. Now, when I started experimenting with it, not only was it coding, it was actually doing my product management as well. And I started using it for other things. Soon it became very, very clear to me that don't waste your time pitching investors, right? This tool is available. Take this tool as far as you can. If you need investment, you need investment. But take it as far as you can. You know, use it to build your product, and it completely changed, you know, my perspective in terms of my startup, being able to take my time off chasing investors versus actually building products that I can ship to market and so on and so forth. So, from that perspective, it's become very, very powerful. So imagine a black founder, statistically, I'm not gonna get a looking as much as you know other uh other communities, right? Yeah, um, so this way I'm using AI to offset that disadvantage. So I'm actually practically building stuff. I'm not complaining about investing or how the investment game is working against me. AI is actually giving me the platform to compete.

SPEAKER_02 11:02

Yeah.

SPEAKER_00 11:03

And for me, and that's the reason why I'm saying that, the underrepresented community need to get crazy obsessed about AI. You know, it's the most powerful platform that they can use to level up. Um at the moment, I'm I'm experimenting with using AI for a whole lot of things, right? And it's giving me the power to even start dreaming about things that I thought wasn't possible. Now they're in the realms of possibility. So it's truly very exciting. So for me, anyone who has the right attitude, AI just prevents you from moves you from complaining to actually taking action or at least try to do something for yourself versus you know sitting on the sidelines.

SPEAKER_01 11:48

Wow, this is so inspiring. Really excited to have you share all these useful knowledge, which I believe will be fantastic for the wider audience. So this brings us to the core of our conversation. So you said can AI reduce or end discrimination? Let's tackle that head on. What is the most compelling argument for yes, it can?

SPEAKER_00 12:14

So, yes, it can because you you have this tool in your hands that you can design it to behave the way you want it to behave.

SPEAKER_02 12:24

Yeah.

SPEAKER_00 12:24

Okay. So, and I'll give you an example. I was um I was using um an AI persona. I wanted an AI to take on a persona as um a data analyst or a data engineer. And then the name that is suggested was Alexandra Chen, right? Yes, and then here I was straight away, it hit me that it's probably been fed a lot of information that, you know, Kofi or Ekojo or um an Ade, um, you know, someone from maybe South Africa with a traditional African name, they've not been fed an algorithm to tell them that there is, you know, Africans who are engineers, also, right? Yes. What I did was I was able to change it, change the name from Alexandra Chen to an African name. And I was able to create a diverse list of names, right? Now, what the AI has given me is the AI has shown its bias straight off. And I can deal with that. I can take that and design a solution to counter, to counter that bias. And that's why I feel like it's a very, very powerful tool with all the risks that comes with it and with all the obviously the data that, you know, has been trained, um, that has been used to train the AI. Look at all the history of humanity, even when you think about the recent, you know, the information on the internet about Africa, about black people, so on and so forth, it would naturally pick up on those biases. But I think that you can tease out that biases and you can actually design a system that can actually change it, tune it out, or retrain it in your context, right? And that becomes a way that you can actually solve discrimination by yourself. Practical example, especially with our platform, one of our key principles is that whenever we do, so we analyze profiles and we recommend them as a shortlisted candidate or not. You see every data that tells you why this person has been shortlisted. And why are we doing that? We're doing that because we want to hold the AI accountable. Where there are mistakes, we want to see it. Where there are biases, we want to see it. Because the more we see it, the more we're able to deal with it. Okay. So for me, that is the most powerful case against it. With all its risk, you can actually fine-tune it, tune it out, or recalibrate it in a way that you can actually see it. Or it can address fundamentally a disadvantage, a discrimination, or someone being treated unfairly.

SPEAKER_01 15:16

Wow, that is amazing and a very optimistic decision that we can design algorithms to be intentionally blind to the biases that humans hold, whether conscious or unconsciousness. And is the idea of using technology to build a true meritocracy now for the other side of the coin? We've all heard stories about biased AI systems that perpetuate discrimination in hiring, policing, or lending. In your view, what is the biggest single biggest risk we phase of AI making discrimination worse, not better?

SPEAKER_00 15:59

So, like the the biggest risk for me is that inherently it's been trained by buyers data. Data that perpetuates perpetuates um old narratives that are wrong, perpetuate um assumptions that are fundamentally wrong. So that's the biggest risk that date like AR has that sort of like native, native risk of it coming with that sort of uh biasedness. So at the first level, that is really what needs to be tackled. So, you know, if you're training your model at a foundational level, you really have to pay attention to uh the quality of the data that you're using to train the data.

SPEAKER_01 16:43

Yeah.

SPEAKER_00 16:44

If you start seeing AI suggesting anyone's name for an engineering role, then you know that like you're getting somewhere. But I think we need to think about how we deal with that like out of the box, um, out-of-the-box risk that comes with AI. And that is one big thing that I think that we're very we'll find it very, very hard to uh to eradicate. Um I said that control layer gives us some hope, but it doesn't eliminate things completely.

SPEAKER_01 17:17

So you you made a really good point there that there's this critical danger that we are simply encoding historical human biases into a black box and then calling it objective, you know. It's a whole reason we advocate for intentional governance and I agree because without it, EI just becomes a high speed automated version of our own worst instincts. Yeah. So moving on to you know my view of what is that leader's playbook for equitable AI? For a leader listening right now who's building or deploying an AI system, what is one question that they should be asked by their team to test whether their product is likely to reduce bias or amplify it? So see this as the litmus test for equitable AI.

SPEAKER_00 18:20

So I think the key question is understanding the risk profile of like the foundational models that we're using. What is the risk profile? And for me, I start off with a very, very bad sort of like scenario set, which is the AI probably doesn't know that females you know can become engineers, there are blacks who are engineers in top of industries, like all these innate biases, they come out of the box. So if you're looking to build something for humanity, for people to use, actually, I think that you need first of all, you need to know the risk profile of what you're dealing with. The second thing is um you need to have a set of governance principles. You know, what are you using AI for? Like, what's it, what what is it, what is it for? Is are are they being used for credit? And that credit, are you making that credit accessible to all? Or are you making it accessible to only like a few people? Like, really, your use case um needs to be very clear. Once your use case is clear, then you identify what sort of governance uh do I put in place. So, for example, for us at True Talent, transparency has to be core. Why? Because in a course of reviewing profiles or looking at all this, the talent market. The talent market includes everybody and everyone, like literally everyone. And I think at a fundamental level, everyone deserves transparency. They need to understand why certain things have been done. They need to understand, they need to see that data for themselves. Whatever data that is shared with a recruiter, that same data should be shared with a candidate. Not processed in between. They need to look at the same view, okay? Because when you do that, you're laying bare like the inner workings of your product, right? And you're able to challenge your systems, you're able to um make sure that you have complex, um, you know, the complexity are unveiled. Mistakes, you know, are unveiled as well, so you can tackle them head on. So for us, like a core part of governance is that transparency element. It's incredibly important. If it's not in if it's not transparent, we should not be serving it to a client or to so if you go on a platform, if someone is shortlisted or they're not shortlisted, you get a full view of why. You know, with evidence. You know, do they have the skill set? Did you see it on a profile? You see it. The next step is in conversations like this the AI taking notes from the conversation, being able to provide quotes and say this is evidence of what the person said, and it aligns to this skill set or this experience that you're looking for. It has to be based on evidence at a foundational level. And that's like, like I said to you earlier, it's one of the primary principles when it comes to our data, um, our AI governance. Another thing as well is when you define your use case, you really need to understand the profile of individuals that you're serving. It's it's very, very important. If you're serving a community, let's say, that do not understand AI, I think that it's incumbent on you to add that education piece, to educate them on, you know, like your tooling, educate them on, you know, they're entitled to know, right? At a very foundational level, what is being used here. Uh so for me those are those are some of the things that I look at.

SPEAKER_01 22:04

Lovely. Just thinking it's brilliant and a practical, it must test which every business should consider. And even more so for your business, which is looking at, you know, the humans, you know, recruitment. There's such a huge impact, positively or negatively, when AI is being used and it affects people, where biases are being incorporated subconsciously. But then again, before our recording, I did mention that you find the you know, products being created, AI tools or solutions being created. And at the time of development, the reason behind its development did not require certain measures or governance. But then later down the line, this tool develops to and becomes something that is being used by a much wider audience. And the question is were those uh or are there continuous governance review being done to ensure that the decisions made at the time of development has been revised to incorporate the impact on these new users. And so it's really important that you shared one of the core, I would say, litmus tests that should be incorporated in any business, in any solution at all where AI or technology development is concerned. So thank you for sharing this.

SPEAKER_00 23:29

And then there are also like emerging risk, yes, emerging use cases that originally, like you said, were not on the template. And then, you know, and governance, I feel like governance is going to be that powerful piece that bridges the gap if it's done properly, right? Because the governance would hold AI accountable in terms of how it's used, so on and so forth. But there's a critical point that um I wanted to make as well. The AI is so intelligent, you know, it's a knife essentially. It can be used for bad and it can be used for good. Now, if it wants to commit fraud, right, it would understand every context, it would understand every human behavior. So, for good or for bad, it can be a very, very powerful tool. And we need to be very, very familiar. I mean, the intelligence level, it would it would just be able to predict our next action. So imagine an AI that's able to predict your next action point, right? Or can predict 10 action points that you might take. And then what happens for all of it, even before you make them. So out of all the 10 action points that you take, you might do, you know, the option six or option nine, and it's got a reaction for you, right? Or the next step action already. So these are things that when you look at, even though it's exciting, at the same time, I look at it as a knife. It can be used for good and it can be used for bad as well.

SPEAKER_01 25:08

Awesome. This is great. Thank you so much, Pius. That takes me to the next question around building the right team, and as you know, that can also have a big impact when it comes to the output of the solution being developed. How important is it to have a diverse team building the AI in the first place? And can a non-diverse team ever truly build an equitable algorithm? Let's take the first question. How do you how important do you think it is?

SPEAKER_00 25:39

So this is my fundamental belief, right? Regardless of AI or not.

SPEAKER_02 25:44

Yes.

SPEAKER_00 25:45

If products, if a product is being used by an audience, the people that are building it should reflect, should reflect that audience, right? And what does that mean? It means that you're building with empathy, you're building with an awareness for all the um all the actors within your client community, right? So at a foundational level, I believe that the more diverse, and when we talk about diverse, uh diversity, it's not just about physical diversity. You know, we're talking about every single diversity that you can think about, right? Being on the table. And I remember we worked at a we're building a product where it was a fintech product serving a wide range. A big portion of that product were a female audience. And I remember one of the lucky things that we had in that team was the fact that we had a female engineer. And then she came up with this idea that look, the categorization of how people spend money, there is a woman's perspective in terms of how she categorizes her spending. Like, for example, beauty as a category, right? Men would probably not have something called beauty, it might be something else, okay? Yeah, but just for her to bring that out, and her being an engineer, imagine if she wasn't in a team and bringing that perspective, right? And then being able to add that to that product, uh, to bring that into the product. That is the diversity I'm talking about. You know, she she's reflecting the audience that are using the product, and she'll bring that empathy into that product build so that it serves the customers really well. If you're building a product for a certain community and then they are not and you're not seeing them in your team, I think that you should start questioning. Um, this is not, and this is where I think DEI has gone wrong. It is not about diversity, equity, inclusion. It's about empathy, it's about the community that I'm serving, right? You're using the same yardstick, but I don't think you should reduce, you know, your quality, you should reduce your criteria because there is, you know, an ethnic minority person involved. And I think that as someone who works in recruitment, I feel like we should really fight back on this perspective that look, it's not about reducing standards. You maintain standards, you have to maintain the best. There's no substitute for finding the best, right? But it's about going out there and then making sure that as many people are considered for opportunities as possible without reducing standards. And I think that's a very powerful point to make.

SPEAKER_01 28:29

Awesome. I love that. And the example you give is very, you know, practical and relatable. I'm asking because you and I know back 10 years ago, tech and AI, 15 odd years, much more so than now, highly skewed towards a particular, you know, um demography and less of women, women of color. And so could we then say what has been built back then are not equitable, or do you think that it can happen that non-diverse teams can still build an equitable?

SPEAKER_00 29:09

I'm very, very skeptical about the potential of um a team that are not diverse building something that is truly diverse. I'm only skeptical because the experiences that it can bring to the table.

SPEAKER_01 29:23

Yes.

SPEAKER_00 29:24

The different perspectives that can be brought onto the table, would it be limited or would it be enhanced? Uh I would argue if you have one subset, right? If you have a white only, I'll call it out, you know, let's call it out. If you have a tech industry that is very, you know, white-centric, um, without any sort of any other players uh being involved, I think that there is that risk of like, you know, what are the options that were put on the table? What are some of the considerations that went into building the product? But in saying that, you also come across, um, and and I I really don't I'm not a big fan of like reducing it, reducing the conversation to just like whites and blacks.

SPEAKER_01 30:08

Yes.

SPEAKER_00 30:09

It needs to be more elevated in a sense that um we should be you should we should be calling out facts, right? We should be calling out what is being missed. And in my in my point, the point that I want to make is this I'm questioning, you know, without diversity, what is on the table? You know, what is the premise, what is the foundational premise of the product being built. And and to be honest with you, we've seen, but the data suggest we've seen products that are hugely popular that you might go in and have a look, and it would have no relate. You know, the diversity is not as anyone would expect. But, you know, they've been able to build a product that everyone can use. So there's always, even though I'm skeptical, the data that could be saying something different. But then again, there's also data that says diverse teams versus non-diverse teams, uh, the difference. You can you can tell uh the difference when it comes to the output.

SPEAKER_01 31:11

Um and I think this was a very interesting question simply because it got me reflecting on whether more advocacy should be made on tools that have been done in the past, built in the past, to be really viewed in the interest of equity. Because, like we said before, I alluded to when tools are built with a particular use case in mind by later on, it expands in its impact or how it's been used and people that use it. It's only fair that we advocate for a continuous review of the decisions made in the process of the development of those tools to ensure that it's a bit more incorporating than it was before, um, of the diversity of thoughts of um and and ensuring that it gives everyone a fairer um chance. So again, it's my thinking around we're looking at AI and the excitement of it being used these days, but then what about the stuff that have been done in the past? And are we going back to make sure that we've reviewed it with a new lens and ensuring it is still fit for purpose? Because whilst it's giving you information, you want to make sure it's actually making the better decisions rather than just relying on what has been used for in the past. So I could agree with you more on your thoughts around you know being skeptical around the team, the diversity, and how it's gonna impact on you know the outcome. And again, you know, there's this principle that you cannot solve a problem you don't understand from a lived experience, right? It's always easier to very quickly come from a place of empathy when you're addressing something that you've lived, which is why I always advocate for allyship, because we cannot all be the same, but we need to learn from each other in a way that I can relate with what you're sharing, even though what you're sharing is more of your it's it's alien to me. I might not be able to ever live your experience, but I should be able to understand it. So again, allyship becomes important because it ensures that we can all um add the best about our bit when we are thinking and bringing these thoughts into into lived products that can impact people.

SPEAKER_00 33:49

Yeah, and representation is very powerful here, like massively powerful.

SPEAKER_01 33:55

Yeah.

SPEAKER_00 33:56

And you know, when you look at the tech industry, you talked about allyship, you talked about advocacy, and you see a lot of work that goes into trying to increase representation within the tech space. Um, lots of organizations are doing great work to make this happen. Because for me, when you think about technology, it's meant tool for humanity, right? It shouldn't be a tool for just a subset of humanity, it should cover every humanity. So I think that the more people we have under the tent, the better, right? We have more diverse products, we have, like you said, lived experience. We have products that you can tell from the build that this is lived experience. Like if you think about me as a black person in recruitment, right? Sitting on the other side, being the one reviewing profiles and everything else, I wanted to build a tool that will protect people like me, right? Because I want to know when I make an application, two things happen. It's either I'm selected or I'm not, right? And if I'm not selected, I would love to know why.

SPEAKER_02 35:09

Yeah.

SPEAKER_00 35:10

Genuinely, I'd want to know why. You know, and I've had lots of interviews, I've had lots of, you know, over my lifetime, like I've been rejected for lots of things. That I would love to know why did I not, you know, why did I not, what was, was I not successful? Why did I move forward? And for me, bringing that lived experience of the frustration of someone like me not knowing what goes into a decision led me to this point to say, look, make the data better, like lay it bare. Let everyone have a look at it. Let them, even for me, like one of the features that we're looking to build in a product is um is to allow anyone who is looking at the data or consuming the data to be able to say if this data is correct or wrong, you know, almost like a like or dislike. Okay.

SPEAKER_02 36:00

Yeah.

SPEAKER_00 36:00

At a very fundamental level. And then using that data, not only, you know, giving the person the data, but let's say, for example, if they were rejected for a role, being able to support them. Right? Can you bring some insights? Can you bring what you're seeing in the market that perhaps they don't have on their profile? Okay. Oh, we are seeing a lot more people who don't have experience in software engineering. We're seeing them taking on side projects, we're seeing them exploring things in their social circle. So, for example, someone has just graduated from software engineering, they're complaining, you know, I don't have any experience, right? And you reject them for not having experience by being able to say to them, hey, um, I see that you have no commercial experience yet. But maybe explore, you know, your social team. Do they have like an app that can help you guys organize, you know, if you belong to a church, does your wife, uh, does your uh church have a website, right? Build them a website. Do they have a way of registering who came to church the Sunday or who didn't? Those who didn't, why did they not come to you? What is it because they're sick? Can they send a message to, let's say, your special team that looks after people who maybe have not been coming to church for some time? It doesn't need to be human remembering it, but it's a system flagging it, right? These are things that when I speak to people that are rejected for software engineering roles and their new graduates, these are the things that I tell them. I tell them to explore the world around them. Go and experience. Like, if you know a friend who is running a starter, you know, take on a data analyst position. Tell them, look, you know, I want to see the kind of data that you're processing. Like, get yourself through the door. Find something to do with that talent. Again, so for me, it's about that transparency element, bringing that data to bear, but not only just bringing it to bear, but using that data as a platform to add value to the person.

SPEAKER_01 38:02

I love that. And I think to summarize it all, I love what you said, and you've shared so many gold nuggets, knowledge, and learnings to help um people who are still on that journey in their careers. I think to summarize everything, a diverse team isn't just a nice to have. It's a fundamental requirement for building technology that serves a diverse world. So to bring it all together, Pius, what is the one commitment you would ask every leader to make today to help ensure the AI to build is a force for empowerment, not discrimination?

SPEAKER_00 38:42

So for me, I will plead with leaders to question the opportunities that the product that they're building, and they should think about the impact. Is it elevating or is it not elevating? Especially if it's if it's not elevating, they really need to think about should I be building this? If it is elevating, then it's welcome. Is it bringing opportunities that were not previously available to people? So, really at a fundamental level, you should be questioning the impact, right? Positive or negative. If it's negative, then you really need to revisit the idea. If it's really a positive, and because I fundamentally think that I want to I want every professional to be able to have control over their career, okay, at a fundamental level. Um, if they're in a place where they are very unhappy, they are not celebrated, okay? Yeah. Or the progress, you know, the progress where they feel they need to be at, it's not moving fast enough. I want to have a platform that can give them that power. Right? There are individuals who move faster in their career than others. You could see a 25-year-old who's a CTO, right? And you could see a 25-year-old who is a junior developer somewhere. Everyone has got like different speeds. Okay.

SPEAKER_02 40:08

Yes.

SPEAKER_00 40:08

We want to be able to build a platform that is able to work to the person's interest, work to the person's um, you know, um strengths, what is available to them. So it's all about elevating. And if we cannot do that at a at a scale, then we should not be uh, we don't have the right to bring this product to market. So that's my challenge to every leader. Question and challenge the impact of the product that you're building.

SPEAKER_01 40:38

That is a powerful commitment and a perfect place and thank you for bringing such a vital and personal perspective to this conversation.

SPEAKER_00 40:49

Thank you so much for the platform to share uh some of my ideas and so on and so forth. Thank you so much for the opportunity.

SPEAKER_01 40:56

Awesome. And I look forward to having you on this podcast in the future because you've got a wealth of knowledge, both with your personal journey and also with building through talent and the awesome work you're doing to serve the community.

SPEAKER_00 41:12

Thank you. And I really want to highlight the important work that people like yourself do, which is the governance piece. If you look at the times that we live in, the governance piece is so, so critical. How do we make sure AI is aligned to our interests? How do we make sure AI is meritocratic, right? To everyone, as many people as possible. How do we make sure that you know our eyes are open to the risks that AI presents to everyone? And I think that you're working in this in that space where um, you know, um I cannot wait to see the accountability, how AI is held to account and make sure it's aligned to your amazing Pius.

SPEAKER_01 41:54

Thank you so much. Well, what a profound conversation with Pius. My key takeaway amongst many, many is that building equitable AI is not a technical problem. It's a leadership challenge. It requires intention, empathy, and a relentless commitment to asking the hard questions at every stage of development. So to connect with PIES, please visit the links on our show notes. You can also connect with Pires on LinkedIn. And if you are a leader committed to building your own intentional equitable technology strategy, you can learn more about our fremos at Anaya Group by visiting anya.org. Until next time, keep leading intentionally. Thank you.

SPEAKER_00 42:43

Thank you.