Responsible AI Is Good Business — Featuring Wiebke Apitzsch

Yeah, welcome back to the ANSI 65L, the podcast where we explore Microsoft, AI, Cloud, Business, Transformation and the people sharing the future of technology. Today's guest brings a perspective. We don't often hear in AI discussions. While many conversations focus on bigger models, more agents, faster automation, she asks gifts and questions. Should we build it? People can people trust it. How do we make AI actively useful inside organizations? Joining us today is we've got a bit, well, founder of AI Impact, AI strategy, advisor, executive coach, keynote speaker and someone working right at the intersection of technology, philosophy, ethics and business strategy with experience from top tier, consulting, including BCG, excluding coaching at in-seat, ongoing philosophy studies and deep practical working, helping organization adopting generative AI responsible. She helps leadership teams move from AI hype to AI impact. Today we will explore responsibly AI, Microsoft co-pilot, AI agents, company culture, trust, ethics, leaderships and the future of work, and much more. We've got come to the ANSI 65L as M. Hi. That was a very long list of items to cover today. Looking forward and let's see how far we can get. Yeah, before we get really deep down into the topics, can you tell us a little bit about you and why you are fascinated from technology? Working in technology was never my plan. After school I didn't really have a clear idea of what to do. When I went into the hospitality industry and I think what I took from there is customer first. I think you don't learn it as well anywhere else. Then I started to stumble into my first of the three topics, technology, when I worked with little hotel databases to do CRM and moved to BCG where I joined a very specific team which was building little prototypes and models for those very big clients and I travelled a lot and I coded a little not very well but I understood how it works. Also I was able to see a little bit of the strategy consulting as well, how do people set up strategies, how do we move forward? I was part of the team who built up BCGX. That was more strategic work and one other thing I did in between was to look a little bit into philosophy. I did study half a year at the LMU and then I found out that I am not very good at driving cars and I thought taxi drivers are not for me so I stopped doing that and went to business administration. These three topics followed me all my life. Technology, philosophy and strategy because you have to understand what it actually is. You have to think where it should go to and then you have to execute. With one of those is missing, you don't get anywhere. Over the years I learned and I practiced and I did projects and now I really feel it's the moment to talk about it more because I changed so many things that we have to go back to the basics to make good decisions. How is the consulting part influencing your thinking about your thinking today? I think these three topics that I mentioned are very different and business administration and consulting is from a logic perspective the simplest because we calculate with plus and minus and we have like there's one euro and I need to get 120 back so that it's a good case and if we only get 90 cents back or nothing then it's a bad case so from that perspective it's very very simple and but it is very complex in terms of stakeholder management. There's a lot of emotion, various fears because while in technology you can do a deep analysis and find out that this model is the best today in business you are already good if you win at 51% of the cases. Some people who work in how you call market chairs and trading and all that that's what they do. They are 21% right and that makes them billionaires. They are come to coaching and the executing and the being fast and making both decisions even though you don't know and it's really hard to integrate but I think if you build good teams then you can do it. So just to make it one more in a very short sentence the consulting forces you to make both decisions fast and to make sure you execute because as long as you are in a theory you're in academia and you don't make money and we have to make money because else you don't have companies. This is what I learned from that and this is what always gives some pressure to my otherwise more bold thinking. Yeah and now you are the founder of AI Intect. Can you tell a little bit about your company and why you found it? So we are three managing directors and we are very very different and the reason why I wanted to work on my own company is because I always work very hard but then sometimes you do it for someone else and I felt that it would be better to do it with getting the impact for extra hours, not so much the hour itself but the impact, the whole impact. And with my two co-founders I think it's very interesting but also very valuable mix because you heard a lot about myself now but then there's Rutga who used to be a professor for physics and he is very deep in mathematics and logic and models and it took us quite a while to be able to have a conversation because frankly sometimes I just didn't get it but now that I started to study philosophy again that became much easier because mathematics and philosophy it seemed to be very similar and with big ideas like the knowledge machine of light and a sense of one, we have a common ground and we cannot develop models together that will actually work in real life so but it's very complex and then we have Felix who is from the IT area not only, he also is very good at working with clients and thinking into the future but he also knows all these basics like how do I implement it? What is the right database to use? What do we do and how do we exchange data from A to Z? So that's a combination and I think together we can really make a difference because only if you think all three dimensions together you can really create projects that deliver what you expected in the time that you estimated. Yeah, that's interesting. By the co-teams though you have all these different parts there. I think a little bit companies or everybody says they are doing AI, every product has this label AI but yeah, I see it on my coffee machine. It's not great and any value for me that's AI inside but yeah, why is AI adoption still so difficult? I think the expectation and what the tool can actually deliver, they differ and that's for many reasons. So to be honest everyone goes like let's do AI and then people talk to us and we always go like can we avoid AI? Do we really have to use that or can we maybe solve that in a different way? Because how does it press the best? So I think many years we were building very smart models like machine learning, predictions and so on and they did what they were supposed to do but most people didn't understand what happened so they didn't trust the system and we had a problem with data quality but that's what everyone says. So now we have Genie I and suddenly it became useful for sea level positions. So they realized okay if this really improves my email writing then it must be able to do everything and that is not true. So there is this question about how do I train the data and how can I apply it, how should it get access to my own information because the impression is that it can really write a very cool essay about Kant so it should be able to write a short essay about my cups that I am producing but of course it doesn't have the data, it doesn't have the background information, we have to build up a secure system with their confidential stuff in there. So we have a very enthusiastic start and then we have a very heavy low because people then realize it's not as easy as I thought and this doesn't work like this and they stop. I think that's the one thing and the other thing is really a more philosophical question because after a workshop people are often very enthusiastic and they feel like it's start, let's do AI first and let's do all of this automated and then when they come home they feel more like what does it mean for my company, for my colleagues, for the future. If I apply this cold bot to all of my processes, what will my anchors just say when I arrive in heaven someday? And then they hesitate, it's not really that they actively say no but it's more like an unclear gut feeling, what am I doing to my company? And I think this keeps many people from execution. That's why we also offer this ethics guideline and introduction workshops with some philosophy thinking and that's why we asked the question, who do we want to be with AI going forward? And what is my personal view on how this company should operate with Gen AI? And the big question that we have to ask there is what is mankind? Who are humans in this company? And what activities should be only for humans? Not less if AI could maybe from a technological perspective cover some of it or not. So once we dice them out, it typically becomes very easy to automate everything else because the very is gone. And yeah, and yeah, I can bring it, I don't know, should I go a bit deeper in the philosophy behind it or is that something new for me, so I would really interested. So one I really like to cite for this is Kant with his thoughts about dignity and price. So he says high level, I'm not citing exactly, but he says everything either has a pride or dignity and everything that has a price can be automated. So I think that is very logical to us, yeah, if I'm just running through, I don't know, automated emails with notifications like tonight, the server will be updated or what. There is no value in having a real person sending that email to 2,000 people, right? And we don't feel that it is in any way disrespecting us as humans to get such an update email. It has a price, it is just a thing that someone has to do to notify me, but there is no personality, no emotion involved. And then there's other things where we interact with humans and of course, they also deliver something with price with value. Yeah. So for example, they call me to say that there's maybe a problem with my bank account. So there's something I would pay for and that's fine. But I always have to treat the human as a human. I have to respect his or her dignity. And by doing so in both directions, we build up a relation and that is something with a very big value. And so if I'm going back to business administration, the two most costly things are finding clients and keeping employees or finding employees, right? These are the two things. So if I don't have employees and I don't have clients, then I don't have a company. So that makes it very clear that this is essential to make sure that every area where I build a relationship, I have to really protect them. Does mean that I cannot use AI in these processes, but I have to use them as supportive elements. Like, if I had said, you know what, I'm really busy whether it's very nice, I will just send my avatar to do this podcast with you. And maybe if it's very well trained and I prepared it super good with all my knowledge and all my personal views, whatever. Maybe in theory, it would perform as good or maybe much better than I do, but the moment you find out that I sent my avatar, you would not be happy. So I am pretty sure that you would not post the podcast and you would be super angry. But for some reason, yeah, and that's, I think that's the interesting point. For some reason, you prefer the real life Deepge with her, you know, spell wording issues, you know, sometimes searching for the right word to say it and maybe saying, ah, we have to redo this. I made a mistake. So you prefer this versus having maybe the perfect bot saying the things I say. And that is why it is because for some reason, the real human matters. Yeah, we want a real interaction, and everyone listening to this podcast would also say, hey, I'm not going to listen to a bot, you know, spreading some kind of more or less, maybe good and maybe stupid content. I will listen to a human being sharing experience. You may get distinction here. So, and this is what we have to put through all our process thinking when we apply AI, where do I need a human? And it is no problem at all. If I, for instance, had a second screen open and maybe I would sometimes cross check and say, hey, what was the philosopher's name? Or can you please bring me my favorite cetacean? I don't remember what that says, what I was like to say. No issue with that if I use it as support, but you want the real person saying this. Yeah. And I would personally encourage every company to think it through to put out where is the value of humans in my company? Protect this. Support this maybe with AI, but not automated and everything else can be automated with no bad gut feeling at all. And will then bring efficiency better results, now higher quality, more automation and all the good things that we actually want from it. I think a little bit, yeah, we had also, I think, the change before it's most the industrialization and there are also this had an impact of philosophy. So I say, I think one of the famous guys, I'd also like, but Karl Marx and so on. So did you think also AI has an impact on philosophy? Well, many philosophers are engaged with, you know, the discussing the role of AI, the question if it has, if it is conscious, the question of how it can be implemented into our world. There's a lot of ethical discussions about how we can actually train these models and if we are, if how we do it is acceptable and I think the short answers most likely know, but it's still done. So there's, there's a lot of thinking and for me, the one big thing is that for thousands of years, the difference that we made between humans and non-humans overall, yeah, was that humans are those with a language and the ability to think rationally. So my dog is super cute and he's very empathetic and fun and loving and all. And we could maybe discuss about the language because I think science found out that animals do have their own languages, but they don't have this intellectual language that don't know, writes philosophical texts about our future or statistics and mathematics and do you think so? I think most people would agree that animals don't do it. Maybe they think it's stupid, but or they don't, they're not able, but they don't do it. So this I think we can agree on. And now this typical distinction becomes problematic because we suddenly have a new kind of creature. It is not furry. It is not sweet and it doesn't have like a nice tail waving, yeah? But it does something that is at least similar to our thinking. So again, we can make, we can, our books are written, tons of books are written about is what Jenny I does, is that something like thinking or is it conscious or not? And I think that's a very good philosophical question. It's not very practical question because in essence, yeah, I know a few people who never shouted at their printers because the printer goes like, I cannot print, I don't have magenta and you go like, it's black and white, go do it. And the printer goes like no. So and we feel then that this printer is a super bad person. So you make it, you make it a person, you give it a personality, you say my printer is evil. Yeah? So how on earth should standard human beings, yeah? We shouted printers, make a real life distinction between a system that tells them in a very empathetic way that they are sorry and not, yeah, humanize it in a way, yeah? And make this clear distinction like, oh no, it is just, it is just working with statistics and very good assumptions. And that's why it wrote me this message and I have to cross check it. The better it gets, the more we will feel that it is like us, even though it's not, but you still shout at the printer. So it doesn't make, it makes a theoretical difference, not a real life difference. And that makes us so important to create a new way of framing the world and make a simple distinction why we are still in a cool and thoughtful mood. So that once we get into the angry mood and maybe into the stress, we don't make mistakes by making an AI, giving an AI the same role as a human. Yeah? And I think that's the big philosophical challenge right now to create this new anthropology and everything else comes after. But there's a lot of after as well, or in the same time. Yeah, that's really, really interesting. I think one big topic is the ethics and a lot of people are talking about ethics in AI. So there are a lot of problems, but one of the symbols is what role do human world play in the future? I think the one of the biggest questions a lot of people have. What do you think on this? Yeah, so I think the role in the future is not an ethical question, but a practical question right? Because I know that many, many people call themselves AI ethics experts also. And most of those people do is to say, I think this is bad or I think this is good. Which is not, which is an opinion, but not ethical thinking. So if you were to think about what is a good way or a bad way, so is this acceptable or is this, and how should we treat it? How should that, what should we do? Yeah? There you have to give a broader, you have to go into a broader scope and you have to distinguish what model you want to use and if what is good for you and all these definitions, which is lacking very often. But this question of what role will it play in the future is a practical question. So, and there, I think it's, get two options. Yeah, so one is people who don't, who don't care so much about the overall good in the world, yeah, can automate many things with AI that should not be automated with AI as a that's what I described before. But also those people will run into clear boundaries. And this is what I explained before, so if you destroy your client relationship by sending the CEO of your biggest customer, a sloppy AI email with maybe even wrong information, you may use this client and then you don't have a company. So I think that we will, or I hope, that we will move into a future where from clear economic, for clear economic reasons, we make sure that the transact, that the relational elements remain relational, and where humans have to do things that are very hard or not possible to create with AI. So for example, to make comprehensive decisions, to make innovation, like really go into new things. And the reason why I think it's realistic is because only humans have actual pain. So if I want to bring in a business innovation, I can of course use stochastics and say what other options are there, and then I can choose one. But a machine doesn't feel pain. So the machine does not go outside and say, this is a cold day, I don't like that. I need a jacket or the machine doesn't sit and say, I'm bored, the machine doesn't sit and say, I'm hungry, yeah, or, why do I have to gain weight if I eat fast food? Yeah, I'm not happy with that. So this kind of impulse is something that really remains humans because the machine is happy, yeah? And if nothing happens, you can put things into their base code to search for something, but this still comes from the human to say, I want you to always be kind or so. Bringing impulses will remain human, to execute, to check, to test, also to test from a stochastic perspective, like what newsletter works better, to sell my product, this can be done by AI. And to build up real relationships, to make sure someone buys from your company, even though others exist, remains human, to then execute tasks like copying things from A to B, and making sure the newsletter goes out every week, maybe even following up like PMO task, this could go to AI, but to, then we cite, do we want to do this or not? Do I purchase this piece of land? Do I build up a new piece? This is something that will also remain human because in the end, there is one person that has the money and this person in the end will want to decide what to use the money for, yeah? So I think if you follow this route of thinking, you can come to a more or less realistic version of the future without complete dystopia. - Yeah, I'm gonna move the money here. I have tried to give one of my agents his own money, but he doesn't spend it very well. (laughs) He did it for all. Yeah, I have to try it again. But yeah, I think a big topic is autonomous AI, how autonomous should AI be, and is there anything where we have the man in the loop or a person in the loop? And yeah, I think this is my first question. (laughs) - Yeah, I think this autonomous AI, I don't know where the value would be to have it, yeah? And as you just said, so you gave a certain amount of money to your AI and you decided that this is kind of risk money. And so it sounded as if it was gone, can happen, yeah? It can happen that you make a lot of money with that, but we also sometimes invest in risky shares. And if you're lucky, we get it back. If you're unlucky, we don't. And there's some kind of advantage sometimes to do it like this because agents are not so emotional, so if you give them a clear advice to always sell if X happens, then they will do it and not be like a human, as it's all, but maybe it comes back, yeah? So that's why from a statistical perspective, it could be a benefit to let go in certain areas and give clear instructions how to act and then rely on the thing to act like this. But so why would you want an autonomous AI? So you want maybe to improve decisions and you can do that for example, if you have insurance companies who have to pay out certain amount of money if something happens. And then if the case occurs, your customer will send in a report and say, hey, I want 200 euros back because I don't know my dog broke someone's, I don't know, stereo system by running through, anything like this. So then you can give the AI an autonomy to decide within certain ranges and then what you should definitely do in what people also do is to add a tip, like a standard fraud detection system but see if once these decisions are made by the AI, if they're a pattern, trans up, if someone goes like, oh, now my dog is breaking stereo systems every week, yeah? So this would be then cutched by the fraud detection system and by doing so we can likely save a lot of money, be more efficient every now and then we pay too much. That's okay because the overall saving is better. And as you maybe put a threshold to say 200 euros, we know it's not gonna break the system, it's really directional. And yeah, and the really bad guys you cover the fraud detection. So it works autonomous in a way, but it works autonomous in a way that we would let a call center agent make decisions. And we would also check on them if there's someone who starts to make a deal with his best friends and, you know, so it's not really autonomous because we set the boundaries, yeah? And I think in that sense, it can really be useful to create kind of autonomous systems that we either control with statistics and fraud detection or whatever, or with humans, whom we actively involve into the work process. So I always call this like a Coca-Cola test, yeah? At least most developers can really relate to it. Because our most developers have no idea how to brew real Coca-Cola or a fraud-cola or whatever they prefer, yeah? But it only takes them one sip or even just, you know, the nose to say, that's the real stuff or are you trying to poison me, yeah? So if we create workflows that give people the opportunity to test the results of the AI in such a way, yeah? So you have a look and without really tracking back the entire process, you know that this is a reasonable result or it is definitely wrong. And you can keep control of the autonomous work in between. You have a person who can really make the call is this correct or not? And if it's not, then you have to do what you always did. And if it's correct, you can move on. Again, this is autonomous in the middle, but not in the decision or in the sign-off. And in this way, I think it makes sense. I really can't think of any reason why I would want an AI to make totally free decisions with my money and maybe running into that, the risk is too high. I have a past, a future, and a today, yeah? And I want a future and I want to be able to create it, yeah? And I can't, if I let agents just do anything that I become passive and I don't like that. - Yeah, I think when we think about business, what see you when you have on your client side, what are the, what's problem or, or, I don't know what to write more, what are the problem? What problems are companies struggling with AI most? - You mean like when they use it? - Yeah, when they start with the adoption, the thing AI in the company. - Yeah, I think that a big problem is that everyone is driving companies crazy. Yeah. There's so much buzz about what model to use, what is the newest trend? How is the best picture creating, LLM going and when will they release something new? So people get totally lost in trying to follow trends. And the sad thing is that from a strategy perspective, nothing really changed, yeah? So from a business perspective, I still have the problem that people have since Stone Age, yeah? The problem that we have since Stone Age, or maybe a little later is I have something here, an item that cost me 90 cent to produce. So I have to sell it for 110 so that in this year, I can still survive and, you know, maybe grow my business a little bit. The problem did not change at all. So the best thing that companies can do is to say, hey, let me review my strategy and my numbers as always and find out where my problems are and where my opportunities are as always. And only then I asked the question, okay, maybe there are some things that last year, I think I couldn't solve, but now having a new tool, Genie I, maybe it's not possible to do that in an efficient way. My colleague, Rutka always says, from a technology perspective, most questions that people give us can we solve with Genie I tools that are three years old? Yeah. We have not, not even, we are not even close to solving all the problems that we can solve with technology with four year old technology or even older, yeah? It's not the problem. So the business problem has not changed. The way we can find out what the business problem is, did not change, people are completely confused and looking into the stars and trying to find out what the newest models can do, but they don't, you need them. Typically, typical problems that are easier to solve with Genie I, are text-based, are in areas that are not GDPR relevant. So anything that's ore that have reasonable data size and can be run with local models on local service, yeah? That's the high level advice, not for if that helps, but other than that, just keep to business and then just see what problem needs to be solved and then we see if it's Genie I or not, yeah? Above our road, would you say, actually, in implementation, AI, is it a technology problem or is it a culture problem? It's a culture problem. Yeah, I said, I think very often it's first of all, it's a, who do we want to be with AI problem? Because that's where the hesitation comes from. So once you're solved it and have kind of an ethical guidelines for these things you want to do with AI and these things, you don't want to do with AI, then you have kind of a calm moment or a moment to breathe because you made this decision and technology and change and everything comes after, yeah? So and sometimes you don't even need change and technology is easy because if you create a process that simply cuts out a piece of work and gives it back in a better structured and maybe easier to solve a way, you don't need change, yeah? I did it with, so we have one of our clients was willing to go through the newspaper with us and we did a very small process there. It was about having a very long text to describe a book and they have catalogues where they sell books. So the task was to shorten this text and so that it fits the catalog, the size and then it should be printed. So what people before us tried to do was to create the full catalog with Gen AI, but that to a problem that people had a look and then they liked the text somehow, but I wanted to do small changes or they were insecure if the text is accurate or not and so on. And so they just deleted the whole thing and started new. So what we did is really think about how the process is good. So what we did is we stayed in the same program that people used to do before. So it's in Adobe Project. And we put in the long text, we shorten it with AI and if the target was 100 digits, you made it 120, yeah, so by default, you made sure people had to change something. And it's easier to shorten something with 100 digits than 3000 because you don't have to read the whole thing and move through your head like what can I put in it? So we got the pattern on how they did it in the past. You put this in. We also gave them the full text as a reference if they wanted to. So now all they have to do is click through, cut out 20 digits where they would like to do that and move on. And this process didn't really need any change because they didn't have to change. The only thing that changed for them is they had to read 120 digits rather than 3000. But if they wanted to, they could still do it for quality reasons. And from a technology perspective, yes, you need, I think this needed like 10 days of development time over all. But it was not anything where we doubted that technology could cover it. So everything like adjusting text, shortening text, making it longer, it can be done. No problem if you put the target first, if you think through how the processor should look like, if then you implemented technology is not this threshold. It's good thinking is this threshold, yeah? - Yeah, we have also the topic. I often heard it's the problem with buyers or hallucinations. And yeah, I think what did you think about this, how can companies or people work with this problem? - So it becomes a problem if you're trying to automate to the fullest potential. Yeah? So in my little example, where do hallucinations typically come from? Yeah, I think the word is really, really bad for the effect because it is not hallucinations are not completely random, yeah? But they come from two reasons. Either the data is not there and then as any good student, it solves it in the best possible way. Or the data has mistakes, or those three, I'm sorry, either just mistakes in the data, or you don't have the data or your prompt is not good enough. Yeah? That's the typical reasons that comes from. So if you set up the process in a way that a knowledgeable human has a look at the final result in a way that it cannot skip it, yeah? You have to avoid that people can become lazy and just say yes, yes, yes. That's why we put in this 120 words instead of 100 because it enforces the human corrective. Yeah. If you do that, you don't have a problem with hallucinations because you have maybe 200 texts that are good. And one in which my human goes in and says, "Hey, there's a picture of a thriller." And the long text says there's a thriller and here in the short version, there's only some romantic bullshit. Yeah, that is definitely wrong. And then you go like, yeah, but last year, I would have had to write 100 articles and now I only have to write one that I now trapped. So in this process, hallucinations are not a problem because we check them. And this is how you should work with Gen AI. You should not try to do something that's statistically impossible, which is to bring a stochastic model to 100% accuracy. If not going to happen with this technology, but you have to take the system as it is and build your processes so that this shortcoming of this model is traced down and that's how we do it. And sometimes I think also human have had my eyes. I think so. So, well, there that's, yeah, I think, actually in the world, all the people think the most evil company in the world is Palantir and they build a system where they can detect where the next criminal act and who are... Most times, who is the victim, who is the evil guy. Yeah. And it's off the track. It's a black people. So, then people say that the system is racist. But did you think the system is racist? Or is it human bias because we don't like to hear something like that? Yeah. So, I think there's many elements to your question. So, the issue with determining where the next criminal activity will take place is of course that you criminalize people who have not done anything wrong. And that is, that this will even create more... that this sometimes creating the bias that it has in the system in real life. Yeah. Because let's assume that you have a city and then there's one area with more criminals and then one area with less criminals. So, of course, you send police more to the area with more criminals which you actually have to do. That's okay, but even more intense. So, now you will maybe even control every habitant of the criminal area and find that 3% more because you never detect everything and you will not control anyone in the less criminal area which will even make this bias higher because a few people who do strange stuff there will not be traced down. And you enforce this issue and then you always get the problem of coincidence and correlation. So, even if maybe the... That's my personal hypothesis. If the root cause for criminal behavior is poverty, yeah. But there is a correlation of poverty and say a certain area where you come from. Then it happens very easily that people assume that the root cause was where they come from and the real root cause of poverty is not seen. And so you put in measures that make life even harder for those people who put them even deeper into poverty, who will even... He will then become more criminal because they have no way... No other way to survive. So this is kind of the palantir effect from my perspective, yeah. And there's more. But if you think about it in a different way, I can also really help because yes, every human being has biases and I like to bring my own example. So there was at work many years back, there was one female that I have to say it. I really hated her, yeah. So the way that person would breathe could make me angry, yeah. And this is absolutely irrational, completely unfair. Yeah, I'm the bad person, you know, her, but I cannot change this emotion. Yeah, so I can tell you for sure that if I was the CHRO of a large company and I would check pictures of applicants, no female that looked similar to that other female would ever get a job in this company. And I even think it is completely fair to say that no female being similar to that female would ever be my personal assistant, yeah. Because no matter how competent if there's no personal, no personal fit, then you can also just leave it. Yeah, it would be a nightmare for both of us. But if you are in such a leading position and you would never in person work with that other person, then your bias is a problem because it's absolutely unfair. Yeah. And here, Jenny, I can help a lot because it only sees the data that it actually sees. So if I build up a system where I cut out all pictures and all, you know, all hints towards someone is living and so on. So I clean up this data in this way before and then I match it to positions. And then I ask my system to say, hey, look from the data you have, yeah, who should take that role and could be that it would tell me like, "I just wondering, yeah, this candidate is best qualified and super smart and lives in the right area and ta-da. Why did you do this, missor? And I would maybe realize that maybe I was biased. Maybe I should just stay out of the process and let everyone else decide, yeah. So maybe my gut feeling is right, but maybe not most likely not. And I think this is really helpful because you can do that. People cannot forget, but AI can forget. So if you realize where your bias, you can make it forget and then you can make it help you to make good decisions. Yeah, I think, or there, there, it's on, actually, in some big metal. It's the HTS, the application, tellers and systems. A lot of people now writing, AI writes a lot of, reshians and fight against the systems from the companies. I think this will, yeah, it will be hard. I have seen it also. I've written my normal ZB all the time and I see after, I don't know, 20 minutes, I get to refuse nights at three o'clock and then I think, okay, you're something, I don't know, a lot of companies work at this time in the human resources departments. I have, I've checked and then I have, I've written all my ZB's like what, what they have inside, they are, they are the keywords and how they describe the company philosophy and then I see, okay, I get more invites to this. So, yeah, I think, yeah, that's a good example for, or for the bias. But, I think a little bit, a lot of people are also confused about, yeah, I think, security with AI, it can expose, I don't know, some data that will not be, how did you think about these processes to secure AI and who should be in charge for this? Yeah, I think it's, sometimes really, you know, how you say it's frightening. If you ask the TedBots that you use more often about, you know, what kind of personality you have or what it thinks you, it should stop doing or whatever, because they have become more cautious and testing it every now and then. Half a year back, they would tell you what psychological problems you have and now they've really been, you know, rolling back and they become more cautious of assuming who you might be or what your psychological profile could be. So, I think that's, it's interesting because it's definitely on the surface only. So, they made their security prompts better. So, the tools don't share so much, but it doesn't mean that they don't evaluate the patterns. And so, this is something that is simply there and we have to be aware that now if I maybe have a fight with my boss and a big corporation, yeah, someone could just take all the press interviews and all the emails that you have and put it into a TedBot and say, okay, how should I best deal with that person? So, we can manipulate much better. And I'm when you play it better, yeah. So, if someone takes the effort, I think the issue about data privacy is also absolutely underestimated. Because, look, if I'm, for example, producing just, I don't know, bubble gum in Germany, yeah. So, and maybe I'm just a small private company with no intention to go big, yeah. And if then I put my financial statement into a TedDBG, open world and train my model, yeah. Maybe it just doesn't matter because if I, I would say like, hey, if anyone's got a question, I do pay my taxes in a very, you know, good way and I'm trying to do my best. If I find out, find out, I can also print it for you, yeah. If that is what you feel, then there will not be any big result because also no, no ruler of a larger company would want to read your financial statement, yeah. And so, how should they get it? And but as soon as we are in either more relevant areas, where, for example, it could be interesting for a US or Chinese or Russian company to find out about your patent, your pending patterns. We have to be aware that they don't even have to get their hands on my actual data to find out what I'm doing because these networks work in a way that on the one hand, I said my question and on the other hand, the answer is matched. So I can read from my answer that is created in my system, what your question is, right? And I cannot really repeat it fully, but I had a longer discussion with an ethical hacker some weeks back. And I was so, I expected that things would be bad, but I was still super shocked, yeah, because I didn't consider this relation. And I think for, for really confidential data, such as defense or so, the only way you can grant data security is to have your own machine in your basement with very thick walls and some other security things that you have to do and then just run a local model and that's it. Because if someone wants to access the data, they can access the data for stop, yeah. And everything else, just how high do I build the wall so that it's maybe too much effort for people to climb it? Yeah, this is how I think about it. Who cares? What if I lose it? And if I building marketing at the ads with Jenny, I then I think, okay, worst case, they hack up book certification in my hotel, yeah. And you hands out, so then whatever, yeah, maybe he buys my product, maybe not, I couldn't kill us. For everything where I don't have this kind of an attitude, I have to consider how, who would care and how, and then I can think about using Jenny, I or not, and my security measures. And of course legal instructions, but I think that's clear, so I'm not mentioning it. Yeah, I think one topic is trust, so who should, should be the state aware of the AI, should the companies, they build it, should the company use it, should be the user, who should be aware of the AI. Yeah, which responsible. And so there's two words, right, responsible in trust. So trust, from my opinion, trust comes from a repeated behavior that is to my liking. So if I'm, many times walking outside my house on the lawn, yeah, I trust that the ground will hold me, you know, and that it will feel in a certain way. And trust that it will be like this the next time again, so, and this, that's a very, very, very, very basic description of it. But for example, if I have a friend, and I get to know that person, then people have this, and humans have this ability to give a little bit of trust in advance. So we say, yeah, I'm willing to trust you without proof. I will test it, yeah. And the more often we interact and the more often the behavior is as expected and good. The more I will say a really trust this person. Yeah, so. And that goes for business if I am interacting with the company. And every time I pay money to do phone calls. And I pay the money and I can do the phone calls, then I start to trust the company. And doesn't it is not necessarily. And then it is not a person, because I can trust an institution, a friend or a soil. And this trust can go very, very fast. If suddenly I pay my money, but I cannot do phone calls. And then comes the question, who is responsible? Yeah, who is responsible? Because responsibility can only come from someone who can explain why it happens. And who can be accountable. So give me my money back, for example. Yeah. So. And this can only be a human because it can also be a company, but in this company, people have done to make the decision that they explain me. I don't know, our network was down and so I can explain network was done. So I can take this. And I can explain what happened. And I give you your money back. Here's two euros for the day you couldn't take the phone call or 50 euros because maybe I miss an important important appointment somehow so they can make these decisions and they can reimburse me or explain all my trust is completely gone can also happen. And what is interesting about that is that we have some lawsuits already and where companies wanted to put this responsibility to the provider of the chatbot. And and a from a legal perspective, it was denied. So they said if we don't care if you work with sloppy chatbot providers or bad staff or if you are, you know, incapable yourself. But this is within your responsibility. So you have to pay it. And more importantly, people also refuse to explain to accept that apology. They say, I'm your customer. And as I am your customer, I want you to fix it. And maybe you can pass the charge to the chatbot creating system. But I don't care about it. Your reputation is at stake and not the chatbot providers because it's you that I have the relationship with. And so I think this responsibility thing is something we really have to consider as business owners. Yeah. And people don't let us get away with it. Someone else is for. Yeah. So, wow, there's also an awesome episode. Oh, you're talking about one hour. So, yeah, yeah, it's really interesting and something new. I never have talked about because I more have to talk about technology a lot. And it's interesting to think about. Yeah. I have a different look on all all this. So yeah, then I will say thank you for for your time and staying with me here. And yeah, give all your perspectives. And yeah, thank you. And have a nice day. Yeah, thank you. Bye. [END PLAYBACK]