The Human-centered Artificial Intelligence in IBM

Welcome to the Business Ethics Podcast, your guide to understanding how ethical principle influenced the world of business and our daily choices. I am Cristina Falconeco, founder of this project, and in each episode I'll be joined by an expert from a variety of fields. Together we'll dive into critical topics like artificial intelligence, politics and philosophy to explore how they shape the future of business and society. Let's begin! Anapala de Jesus Aciste is the general manager and chair of IBM Europe, Middle East and Africa. She is with us today to help us discuss a crucial topic, human-centric artificial intelligence. Welcome, Anapala! Thank you so much Cristina, it's a pleasure to be here today. Before we dwell into the subject, I wanted to have a feeling about your career, who you are, and how the woman, did you progress in an industry like the digital tech industry, which we don't know is mainly a male industry, but also not exactly in an European industry. We always hear about the Silicon Valley of the world, and of course IBM too, is a US copy. So Anapala without farther ado, who are you? Where do you come from? So Cristina, I love to talk about that because technology is something that is really a passion for me. And it started when I was actually very young. My father bought me my first video game in a Christmas day, and then when I was older, he bought me a personal computer, a tutor to teach me how to program. So I think that technology is something that I could say started in my household, started from the family. And with that, I think it became very clear to me that this was going to be the career that I wanted to pursue. So I studied computer science at university, and that's when I had also the opportunity through the internship program to start working with IBM. And in a very interesting time, because it was exactly the year that IBM developed Deep Blue, a supercomputer that won against the Garicasper of the first time that a computer beat a human in chess. And so for me, it's something that, as I said, is very much caught my life because I think I was a very young kid. I was exposed to technology, and I had the opportunity also to, as I evolved, to have access to amazing technology. And fortunately, in an environment where we were looking at technology to support the progress of humanity, to make us better, to make us perform better, work better, drive progress in the communities that we operate in. So I think a very fortunate combination that I think in some ways gave the framework of how I look at those technologies. Which is the perfect segue into our discussion, the core of our discussion. And my question to you is, what are the key elements to ensure a human-centric AI transition? How do we implement AI transition, keeping the human being at the center? Well, I think first, the first topic or the first point that you need to consider is that it starts with the purpose. And I have to make sure that the principle motive or the reason why we're developing this technology is to augment the human capabilities and not replace them. Is how we make humans better and how we combine the best of each one. Right? Machines are very good at reasoning, at making calculations, at, with probabilities, women and men, humans are good at making judgment, needing with them be greedy, which is pretty much the reality of what we have to deal with every day. So I think that combining those two elements of intelligence, let's put it that way, is really what drives the difference. So I think I would start with purpose. Then you have a series of principles that you also need to take into consideration when you are developing the AI, making sure that you are taking care of the privacy of the data, for example, that you eliminate bias from the data sets that you're using to train the systems, that to provide explainability or how the algorithms are reached a certain conclusion. So those are all aspects that are actually embedded in our principles for responsible and ethical AI at IBM, that I think serve very well for any developer around the world to abide by those principles in order to develop those technologies. If we apply this framework of thinking about AI and the human being to business, to cooperation, in particular, the corporation you help in Europe and the Middle East, what are the challenges and opportunities that companies and senior management and all the talent in the company have to face today and can leverage or monetize? Yeah, one of the things that we need to take into consideration is that companies in multiple or matter what industry that they operate in, they are already bare a series of responsibilities. They are already responsible for the outcomes of what their business generates. And when you are implementing a technology that is so disruptive and in some way so autonomous as AI, you have to take those elements into consideration, right? You don't want an algorithm going out there and making decisions that could, you know, a framework reputation that could create a financial impact to you that could drive some level of lack of trust in your business because all of that at the end of the day is going to impact your business. So, I think it's not very different from any new product or new solution that you're going to apply in your business. The challenge here is that it's a technology, as I said before, that has a level of autonomy that you need really to establish different guard rails to support that. And those guard rails also have to have some level of intelligence and autonomy so that you don't depend only on the human being in every step of the way because the volumes of data, the size of those, the computing power that is used to execute those applications is significant. It goes much beyond the human capacity to be able to control each step of the way. So, how you're looking at that is really providing solutions, technologies that also help companies in managing the entire life cycle of AI from the data sets that you're using to train the systems to how the AI is actually performing in the interest state. That means when the user is actually interacting with the AI, that you can really have total visibility of that entire process so that you can take in no actions. Eventually, you have to make an intervention to make sure that an algorithm that is performing different from what it was the original purpose that you can take immediate actions to remediate. So, the more that you can embed those solutions in order to have that visibility and the orchestration of this entire set of components that are part of the solution, the better off you're going to be to ensure that you're deploying AI with safety in your organization. Do you see any difference talking to your colleagues from other parts of the world? Again, I go back to your areas of oversight. Europe, the Middle East, the African, do you see a difference between these three areas? Europe, executive versus Middle East, executive versus African executive on how they approach AI? It's very interesting. I think what you're going to see some variations on the same team. It is concerned about cybersecurity to start with, making sure that the data that they are using to train is protected because one of the major risks that we have right now is exactly the data sets being infiltrated with data that is harmful to the train. So that's one key element. Governance, the ability to explain how the algorithms have reached certain conclusions, especially because everybody is aware that the regulations that have already been implemented, like the EU, AI Act, here in Europe, are that are going to be implemented. Privacy of data, so a lot of discussions around data residency making sure that you have protection of the data that is being used by the algorithms, making sure that data that the user provides as it interacts with the application, that that is also protected. So for example, you have any identifiable data that could really be linked to a certain individual, and that data is properly protected in the system. So overall, all these elements are in. I don't see any geography with more or less, I would say concerns about these topics. I think it's more about the level of awareness, the level of acceleration in terms of putting a more clear regulatory frameworks in place, but I see today almost, I would say almost 100% of the clients really looking at those dimensions before they put a solution in production. And how is IBM, so your company helping the executive running this company through the transition in AI, which provides, we know efficiency, but also the challenges we have talked about. Do you have like a roadmap or you know, a five steps approach, what is IBM advantage here? So if I team up with IBM, what do I get out of it? Yeah, so we have this concept that IBM that we are client zero of our solutions of our technologies. So we are kind of the test bed of everything that you're going to to launch there and that that was no different with with generative AI. So we took an approach to apply to identify use cases within the company where we thought that applying AI could drive productivity efficiencies, allow us to better deploy our skills and resources in areas of innovation, people that were doing, you know, tests that were repetitive, that were not adding value to the company, how to also make it to those so that we could, you know, take advantage of the great talent that we have in things that are driving more innovation, more value to our clients. So one example is what we did in the area of HR. So we created a chatbot called SKR where all our managers can access and had, you know, ask for information about their employees, create job requests automatically. We have solutions also to looking to resumes and support in writing a proper job descriptions of what a job out there, you have clarity on what exactly we're looking for on a candidate. So all those things are part of how we operate today at IBM. Now in order to ensure that we do this in compliance with not all with the United States, but all the countries that you're creating and IBM operating approximately 170 countries around the world, we have created an AI ethics board at the company. This is a multidisciplinary board that has, you know, the technical team has our legal team that has HR to make sure that we in each one of those implementations, we are following the requirements, the legal requirements of the countries that we are operating in. I think that we serve really as a reference case and then it's really a combination of our capabilities in consulting any technology to approach our clients and start really from what is the business logic of this of this project, right, because sometimes we see people, you know, applying the technology for the sake of technology and then they end up realizing that there is no return on that investment or that the business case is not just a project. So we try to support our clients to identify what are the highest impact areas, what are the mitigation plans in order to make sure that this is implemented in a safe way and then we provide the technology that really enables that and that goes from, you know, having an environment where the clients can do, you know, the proof of concepts, test the solutions, train their algorithms to making sure that the technology that we are working on is really a good thing. So we are going to make sure that the data that they are using is properly organized because that's another big problem that we are starting to see. So we are able to evolve past enough because they don't have the data prepared to be consumed by AI and then the layer that really provides, as I mentioned before, the visibility of how these algorithms are being developed, which is really the governor. So I think that we have a very comprehensive set of capabilities, but the most important element is that we start at home, making sure that if it works for us and we are company with, you know, more than almost 300,000 people around the world with multiple functions with multiple services. So I think we tend to be a very good reference case and from there we apply the learnings and the technologies to our clients. So you don't only provide this service in terms of tech, but you are a trusted, a people advisor, a trusted partner that uses their own case study and their own experience and share with the client as the clients if it or it makes sense. Yeah, because it's interesting, right? I mean, the most credible way for you to sell anything is to demonstrate well, how, you know, how exactly what it's all right. And most recently, a couple of weeks ago, we did a challenge, so we had approximately 170,000 employees doing use cases on our platform in the company. So that is really also to make sure that we engage in the entire organization that this knowledge, this technology is not just constrained to a group of people, we want 100% of our organization taking benefit, learning how to use it and scaling and upscaling using this technology. And on the power of doing the business settings summit, we also pick at the future. So I want to ask you, what do you see on your eyes and next after AI? AI, I think we have identified the potential, but to be honest, I think we are just scratching the surface of what can be done with AI. Because when we start really to use this technology to transform the workflows that we have today, some of the processes that are still super manual or super difficult to be executed because you don't have enough intelligence on them. This will unlock levels of productivity that are tremendous. Right. I mean, McKinsey, for example, estimates for trillion productivity gains per year, which is a significant step up. And in my view, very much needed because we still have a lot of bottlenecks that prevent consistent growth in our economies. So I think that that's something that is still in its insancy in what can what can be done with it. We know that there is additional technologies being developed, particularly in the area of quantum computing, which is another important investment from IBM, from an research and development standpoint. And the combination of quantum and AI is certainly going to bring another revolution because one of the things that you're going to start to see is that the limitations of AI will rely on having available data, available data with quality. And we will enter in a phase where we are already entering actually in a phase where synthetic data is going to be required to continue improving the quality of those systems. And that's where quantum can come as a solution for that or as a possible technology to support that. And then I think it's going to be more on the application and the fields of application of these technologies that you're going to see the revolution, the compounding effect of using AI, for example, in drug discovery, in biomedicine, in understanding how our bodies work. Then I think we're going to have another wave, which is really creating, you know, humans that are last seek that are healthier, that are more capable of doing activities while at the same time, having time to enjoy life. So I think that as I said, we are we are willing the beginnings of it and the repercussions and how this is going to transform the way we live is going to be absolutely fantastic and substantial. Yeah, then at least the positive view, especially if we keep the view on the center and last but not least since we started with you and your personal story, what would be your personal advice to aspiring leaders, female leaders in particular in the tech world. Well, first of all, it's as I said before, this was something that I enjoyed, right? So I think that the first step is. trying to identify what aspect of technology brings value to you and brings value to your life, right? So I do think that the world is becoming technology. No matter what profession you're going to take, you can be a lawyer, you can be a doctor, you can be an engineer and I mean, technology is going to be front and center in everything that you do and is going to make you a better professional. Working in the technology field, per se, today I would say it's the major source of opportunity. We have tremendous gaps in multiple areas. If you look, for example, cybersecurity, which to me is probably one of the most concerning aspects of technology going forward. We have a gap of millions and millions of professionals around the world so it can be a tremendous source of opportunity for women to contribute and in my view, it's really a fact that we will need more talent, we will need different perspectives in the way that those technologies are developed and for all of us, if you want to look at from a cause, a standpoint, from a purpose standpoint, the more that we can have those different perspectives coming in and if you want to have a healthy, safe AI, contribute to it. So I think that this would be a good reason for anybody to think about technology when they are starting their careers. And I'm Paula De Jesus-Asciss, Chair and General Manager IBM Europe, Middle East, in Africa, many, many thanks. Thank you, Cristiana, I was great talking to you. Thank you for listening to the Business Ethics podcast where we explored the intersection of business and ethics. We appreciate your interest and invite you to tune into our next episode.