Welcome to tex Stuff, a production from I Heart Radio. Hey therein Welcome to tex Stuff. I'm your host, Jonathan Strickland. I'm an executive producer with I Heart Radio. And how the tech are you? I gotta treat view folks. Today I had the opportunity to speak with Bena Amnath, the executive director of Deloitte AI Institute. Vina is an accomplished technologist and expert in artificial intelligence. She's a coder, she's, you know, an engineer, and she's a great communicator too. She has appeared on numerous shows and panels talking about AI. She's also the author of a book called Trustworthy AI. And this was a fantastic opportunity to speak with someone who actually has a deep amount of experience in the field and really talk about some of the big concepts in AI and get a little more perspective on them. And I have to admit Beena's responses really open up the blinders that I have on And of course I'm like a lot of people, right I I go through life thinking I have a pretty good handle on this. I think I know what's going on, and then I meet someone else who has had a you know, a different experience, and especially a different depth of experience in a particular field and realize, oh gosh, I hadn't even considered some of these specific scenarios, for example. So I really very much enjoyed my conversation with Bena. She's also incredibly good at putting things in a way that are easily understandable. A lot of technologists, when you start to talk with them, they get really heavy with jargon or or concepts. That makes sense if you've had experience working in that area, but if you haven't, your eyes kind of glaze over and you just trust that what they say makes sense. That was not the issue with Bina. She is really good at talking about this stuff on a level that that the average person can easily understand, and yet also really stressing how AI is a very important component today. I mean, we're seeing it rolled out in all sorts of different ways across all different sectors. We mostly talked about business in this conversation, but clearly AI is everywhere. Whether we're talking about facial recognition technology that might be built directly into the camera on your phone, or maybe we're talking about a personal digital assistant, you know, something like the Amazon one. I won't say her name because some of you have her and she gets real like she she perks up when you say her name. Um, those sort of things obviously have components of AI built into them, but we were really looking at things like processes in business where you might need to use automation and artificial intelligence to make complicated processes more efficient and less human intensive. And so, yeah, this was a great conversation and I really feel like I learned a lot. I hope that you all enjoy it. And again, she does have a new book out. It's called Trustworthy AI, and there's a copy on the way to me, so I'm very eager to read it myself because just talking with Bina I felt like I was just scratching the surface. But you're gonna hear all that, So let's get to that interview. Bina. I want to welcome you to the show. I am so pleased to have an expert on AI. Trustworthy AI, no less, Welcome to tech stuff, Jonathan, thank you so much for having me on your show. I really enjoy your episodes, so I'm looking forward to having this conversation with you. I am as well. And one of the things that I like to do is kind of set some foundation for any kind of conversation around AI, because in my experience, and I'm sure you've experienced something similar chatting with people about AI, it seems like everyone has a different, sometimes very specific idea of what AI is. And I'm curious, how do you describe AI to people? Yeah, that's a great question to start with. So AI is a form of intelligence that uses machines to do things that traditionally required human intelligence. So it is artificial intelligence which is created artificially by machines. So now I like that description because it covers such a wide spectrum every thing from sort of the science fiction approach we've all seen about machines that seem to think like humans to a point where they usually become the threat. I mean, that's typically the way we look at it, which is I'm sure going to come into play when we talk about trustworthiness, because I'm sure a lot of people aren't aware how AI can sometimes be a danger, but not necessarily like sky Net from Terminator type danger. Let me elaborate on that description than a little bit more on you know that next level down on AI definition, Right, you know, the way I think about it. There are three types of AI. One is artificial narrow intelligence, which can do a very specific, narrow task that a human can do, like sort a bunch of photographs, Right, That's a very narrow specific task. So that's artificial narrow intelligence. And then there is a form of artificial general intelligence, which is a form of AI that can do any task that human beings can do, right, So it is pretty much everything that a human being can do. And then I think of a third category, which is artificial super intelligence, which is a form of intelligence which is smarter than all human beings combined and can do more things that human intelligence couldn't do. So when we talk about AI in the business world or in reality, it is where we are with AI. It's very much in that artificial narrow intelligence space. But when we hear a lot about the in the media or the hype and the fear, you know, it's talking really about that super intelligence phase, which is a form of AI that is smarter than all human beings combined and has more capabilities that human intelligence. And I think there's a big gap between reality and between where we you know, we are anticipating things to be right and and you know. Part of the reason is, you know, actual super intelligence is a lot of our human imagination, which is where AI was when I was studying years ago, right, So I do think there is value in imagination. I do think there is value in thinking of worst case scenarios so that you can address it. But the reality is we still today don't have the tools or the capabilities to build out artificial general intelligence or super intelligence, and we do not have that capability today. I see parallels in this as well, like I have a very similar description of autonomous cars, for example, like people talk about autonomous cars like we've reached level five autonomy, when really I would argue we're still around level two creeping into level three, but we we are not close to level four or five. And this is why I like having this kind of conversation right up front, so that people kind of set their expectations, because artificial intelligence can already do incredible things in these very narrow, narrow uses, and I'm blown away by it whenever I learned about that. But I do also see the the allure and sometimes the trap of extrapolating that beyond the narrow cases and thinking what happens when this goes beyond that, which it could very well happen, but we're not we're not at that stage yet. Um. But but I think of things like, yes, I think of things like like the image recognition like that to me is still an amazing thing to see developed, Like you know it is. Ever since I started covering tech, the ability has grown so fast. Like I remember when, at least on the consumer side, you might see something that was like detecting a ace, not recognizing a face, but detecting the structure that makes a face and for a camera, And now you know that that looks like stone age technology by comparison of what we're seeing today. Yes, Jarthan, And you I know you've been cowering tech for a long time. You know you've certainly seen seen the early evolution, right, but you know, and look, when I studied computer science, I did program assembly language programming basically using zeros and ones right that level. And you know the languages that I use, like Pascal and Fortran lots, you know those don't even exist today, right, So there's a whole evolution happening. And I do think that is a big component to imagine the future so that we can at least go towards take care of the risk, and focus on all the good things that you know AI and technology can do. Right, So imagination is a good thing, but not the fear a part. And also I think I think what you said kind of is a great message to anyone who's interested in really focusing on AI. The fact that you were working in assembly so close such a low level language, you get you get a real familiarity with what these machines can do and their their potential that I think, uh, you you almost lose when you start working on high level uh programming languages Like you you get so focused on what the programming language lets you do. But if you've worked out at that low level, you're like, hey, no, I know circuits and wires, Okay, I am, I am, I am one step away from this machine. Yeah, we we have to realize, you know, just like you know, when you talk about it today, will talk mostly about the software, but the hardware is also evolving. Right. We certainly don't have wax of those massive back from pure systems that we had to program, right. I think there is evolution happening in every dimension, and you know, it's it's part of the growth of AI or any technology if you think about it. M hmm, Well, I also want to know what you mean when you when you use the phrase trustworthy AI. So what is it that makes AI trustworthy? And what what's the what's the alternative? What is the untrustworthy side? Yeah, that's that's a great question, and that's something that you know. As a technologist, I'm enamored by all the cool things that AI can do because I just focus on all the value creation. But over the past a few years, as AI started becoming real, I also realized that there are you know, with all the good things that can do, there are negative consequences to it, right, and so I put that negative consequences in the under the bucket of untrusted untrustworthiness. Ethics is a big composed under of it, right, Whether the AI is fair or biased or transparent explainable, but also things like is it compliant with local regulations, does it have controls in place? Does it have governance in place to continuously monitor for it going wrongue because you know, Jonathan, today AI is mostly machine learning, so that it's learning and evolving. It's not that era when we developed code put it out there and the code states static and its behavior was very predictable. With AI, the outputs can change depending on inputs your feed and it's impossible to trade on all possible inputs. So trustworthy for me is you know, really, or is when you have addressed, when you have thought about and addressed all the possible negative things that this AI solution can cause. Well, I would love to kind of dive into a little bit more of that because one of the things that you said that really resonated with me was the idea of transparency, because I have covered this past episodes of tech stuff, but the sort of the black box problem of creating a system, for example, a machine learning system, and you have this this machine that's training itself over and over and over. Maybe it's adversarial training, maybe you actually have two systems that are set against each other and you're training, and the issues that can arise if you have distanced yourself so far from what the machine is doing that you are unable to determine the process by which it arrives at its conclusions. And that to me is one of those those pitfalls. Yes, but I would also challenge it a little bit, Jonathan, because that a whole synthesis of my book is that it depends on the use case. It is not a one size fit soul. So depending on where and to solve what problem are you using that DAIR solution, it is for that organization that teams decide if transparency is crucial. Right. If if your AI solution is being used to for patient care in a hospital system, then transparency is absolutely crucial, right. But if you are using the AI solution to predict when an X ray machine might fail, and you're able to predict at accuracy rate that this machine is going to fail in the next forty eight hours of call a technician, transparency may not be as crucial, right. So I think transparency is crucial depending on the use case. And that's true for all the other dimensions as well, even fairness and bias, which we hear a lot about. So it really depends on the use case that that you're using the I fall Hey there, Jonathan back at the home studio, just here to say we are going to take a quick break, but we'll be back with more with Bena Amanath, the executive director of Deloitte AI Institute. Bias doesn't necessarily mean negative, depending upon the use case of the technology. In some cases you need to have a biased system because it's specifically meant to be weighted to do one thing versus another, and without the bias it doesn't do that. But the way we typically hear about bias is when it is making a negative impact, when it's something for it's like like the facial recognition technologies. We've heard plenty about that. So it is interesting to me, And uh, I'm curious, like, what are what are some of the uses of AI you're seeing in technology now that you find really exciting. Yeah, no, I think you know, we're still very early on in this technology evolution and there are still so many use cases to be solved, so many industries to take a I too right to point about bias and its relevance. I completely agree with you that, you know, it depends on the use case, and it goes back to that first question we talked about, right, how AI is really emulating human intelligence, which means that it is going to carry over the biases of the humans that are building it. Right, But as as a business or as an organization who's looking to use an air solution, who's looking to develop an a solution, they really have to, you know, bring together the stakeholders to discuss and decide how crucial is fairness or unbiased ness important in this particular AI use case. And easy one out is if it doesn't involve human data, then you probably don't have to worry as biased as a factor and address it. And if it does involve human data, then again there is weightage in what right if there is biased at in an algorithm that is providing personalized marketing, that you know that there is a weight to it. And if it is if there is biased in an algorithm that is supporting law enforcement decisions, that's a higher rate, right. And it's really about rating it, you know, weighing it and deciding which ones are the one where biases acceptable and you can still proceed and get value from the AI solution, and which are the ones where it is absolutely not acceptable and you need to stop and figure out and alternate way to solve for that problem. It's fascinating because it to me this is starting to sound and I agree with you, like the machines we build are in large part reflections upon ourselves, especially when we're talking about coding and software. I mean obviously that's going that's a creative process. I don't know that everybody views it that way, but I think of it very similar to creating any kind of creative work. It's a reflection of your process and your you know, the things that are important to you, the things you've prioritized. And it makes me think of how we're in an era now where I'm getting a little in the weeds here, but we're in an era where we're more likely to address things like, uh, mental health and the fact that we need to be mindful and we need to improve ourselves. And it's almost like taking that same approach, but applying that sort of thinking to designing a system so that we are being mindful to create the best system for whatever purpose it is it's intended to address. Yeah, you've got it exactly right. The way I think about it is, how can we reduce the unintended consequences? Right? We know there are going to be risk associated with it, How are we going to have a discussion prior to putting the solution out into the world and then you know, see all the negative impacts. Can we have a proactive discussion as part of your project planning meeting or your design meeting right to proactively identify what are the ways this could go wrong and fix it. Johnathan, you know, the easiest example that I can give is we're living in this very interesting era where you know, AI as a core technology is developing and you know, there are all these the value that you're getting from it, and then there are all these negative things that can happen. So think about you know, way back when when you know the cars were first invented, right, we didn't even have proper roads. We didn't have seed belts, we didn't have speed limits, right, and be in that phase where there are cars running on the road. They're taking us from point to point be faster, so we want to use it, but we don't have the seat belts put in place, we don't have the speed limits set in place, so you're going to see accidents. But we are humans. We're going to learn from it and we're going to come up with those speed limits. We're going to figure out what are those card rails, and it is going you know, we are going to you know, achieve a point where you know, we have those guard rails in place so that you can run with AI faster. It's just that this interim phase is when you know, we have to figure it out out in tandem while it's running in the real world, causing accidents. And in some cases that's that those accidents can be things where you have it maybe in a test environment and you think, oh, this isn't behaving the way I thought it was. But you know, thank goodness, it hasn't been deployed out in the real world for or within your company's UH processes, so you think, oh, well, it didn't wipe out all of our revenue because it's in a test environment. UH. And in other cases, I see I see some companies. I'm not gonna name names, Bina, I'm not gonna put anyone on blast here, but I have seen some companies that have taken that kind of idea and applied it in UH specific deployments of technology where there can have some some real world negative consequences to end users. UM. And that to me has always a concern I find, yeah, I find that I find it hits me wrong. Yes, And that's the reality of how we've evolved as a technology in their technology space. It's a bunch of you know, technologists coming together and building these cool, new shiny technologists. Look. You know, as I said, I am a technologist in my DNA my training, and it's very easy to just focus on all the good things that can do. But with AI, now that realization has hit, you need other you know, skill sets at the table, whether it is a social site, is philosophers, legal and compliance to help us figure out those seedbells and the you know, the speed the lanes, you know, because technologies by themselves cannot do it. So you'll see more of the discussions coming around ethics and which is resulting in new roles and new jobs, which becomes core and part of your engineering process. Right, So that scope of who is involved in designing and developing AI is definitely increasing. And the other big part, you know, and this has been a challenge since I started in tech. You know, there's a lack of diversity in tech. It's a reality, right, But unfortunately, because AI is so closely tied to human intelligence, if you don't have enough diversity from you know, not only from a gender, race at necessity perspective, but even a diversity of thought, right, you're the AI solution you built is not going to be as robust as it could be if you had a diverse team at the table. Right, you've probably heard of that classic example of you know, the robotic vacuums, right, how it was designed and now it was built out. And then in the Eastern cultures it's normal to sleep on the floor and it sucked up human somebody who's sleeping their hair because it was never trade on it. It didn't come you know, it didn't come to the discussion, and it was being designed because nobody was there from that culture. Right. So I think, you know, the realization that you need more diversity at the table, you need more controls in place. It's all coming to the forefront. I definitely see companies addressing it. But the the DNA will now has been oh, look at all the cool things this technology can do, let's go put it out right. But I do think, you know, companies are getting mindful about it and hopefully we'll reduce the number of unintended consequences. Yeah. I see the same thing reflected in the open source community, where you have an open source approach to developing software, and because it's open and and anyone interested and capable can contribute ideas get tested, very quickly. New new perspectives get incorporated very quickly. Things that are working stick around, things that don't work get improved. And the way I've described it to other people is, if you have a closed off garden that you're working on, you're only as good as the smart people who happen to work for you. And if you go with this other approach where you purposefully open it up, which is like the biggest version of let's let's try and get as much diversity of thought in here as possible. Uh, you don't have that limitation because you've You've just said, well, now the world is I mean it's not the whole world, but but effectively the world. The world can contribute if if they, if they wish, and uh agree, I think having that diversity is absolutely key to creating solutions that work for as many people and as many potential uses of that technology as possible. I being a a a white man in the United States, I am I am essentially the catered to audience for a lot of tech, and so I've seen how things that were made to work really well for me do not work for some other people. And that's such a tiny little microcosm when we're looking at you know, the GREA and scope of tech which goes so far beyond just consumer electronics. UM I absolutely agree that that diversity is is required if we're going to have a i that is truly trustworthy. Yeah, exactly, And you know, and then but there it's not never. It's never as straightforward as we tend to simplify it down to, right, like when we talk about explainability. There there are real challenges and those are real business challenges on even when you go down the open source route right, a lot of time, if you go too much on the explainableitypath, you know, you you have to still share data and algorithms and those are strategic assets and it can result in compromising your company's i P. Right, it can result in you know, security hacks because the more explainable you make it, it is more susceptible to manipulation if it's functionality is fully understood, the privacy aspect of it, prioritizing playability and you know, how do you make sure you hit a balance of why you are making sure you're mitigating the risk but at the same time protect your organizational i P. That's that's a that's a solution. That's that there is no one single answer. It is for the stakeholders to come together and discuss it and identify were that balances, because it's going to be different depending on your business. It seems to me like you're saying the real world is a complicated place and there's a lot of different shades of complexity to it, and that I can't just simply uh summarize it in a black and white approach, which I greatly appreciate, uh, and that that's interesting to me too. I'm glad to have that perspective because again, like as a as a communicator for tech, uh, I know that I too fall into the same sort of pitfalls of oversimplifying for the purposes of trying to get a concept across, because to really dive into it, you start to you start to feel like they're there are so many threads that you can't see the rope and that or you can't see the forest for the trees if you prefer. But but that's that's very important to remember, and I think it is a great reminder that again, like we said at the top, that the use for this technology kind of defines the approach that you need to take in order to make certain that you're you're getting the result that you want. UM from a really high level, can you kind of talk about your concept of what what it is? This is almost a trick question because there's so many different variations, but what what what an organization's process would be when considering to implement AI solutions like high high level approach. Yes, Historically it's always been you know, how can we use ARE to solve this business problem? And what's the r O I what you know? How much profits are we going to increase by doing this? Or how much costs are we going to save by doing this? Trust me, I've done this project and you know that's how you know every conversation starts because we want to make use technology to drive more business value, right, whether it is through customer engagement, optimizing our existing process and so on. I think the discussion that that if you are serious about getting making your AI trustworthy, the discussion that needs to happen upfront is defining what does trustworthy I mean for for my organization? Right? And uh and it could be different depending on the organization, It could be different depending on the use case. But having those high level principles, and there are plenty of principles out there, there are plenty of frameworks out there, but I think every organization needs to think about what are the key pillars that they agree upon and that they would never want to void it right. And once you have those, then next step is to decide to make sure every employee within your organization understands it. Because it's not just your I T team, It's not just the engineers of the data scientists who need to understand ethics. It's that marketing marketing account person who is looking at using an AI solution, buying it from a vendor to use it within your company. They need to make sure that they are asking the questions which ensure trustworthiness and do they do is the software they're buying, has it been tested for fairness? What was it tested for? So every employee within the organization needs to understand what distrustworthy I mean for my company and how do I how do I make it, how do I use it in my role? So role specific training. And then the other crucial factor to decide, and we've see variations of it in the industry, is you know whether it is getting a cheap AI Ethics officer or setting up an AI thinks advisory board right, making sure that there is somebody who is responsible to keep you know, to keep this moing within the organization is super important. That's more from a people perspective. And then the last thing is really looking at your existing processes. I don't think you need to completely come up with new processes or new controls, but just adding in an trustworthy check in your existing engineering processes or in your existing development process or your procurement process to make sure you're checking for the trustworthiness of any AI that tool that you buy or that you build, you know, having in addition to the r O, I ask Sen spent ten percent of your time to brainstorm on what are the ways this could go wrong? Right? And capture it and when you build that technology, put those guard rails in place. Now it is guaranteed you It is impossible to identify all the possible ways it could go wrong, but even if you get you know the ways it could go wrong, it is better than not thinking about it and not addressing it. So that is a very comprehensive way you can do it. But it is all easy. It fits in with the existing trainings and processes that you already have in your business. Right. I gotta say, like as as someone who is a technologist and uh and coming at this from that angle, that was such a human centric kind of answer. I really appreciate that. I've had a lot of discussions with various leadership around different companies and this idea of of having that explanation and getting buy in from different departments so that everyone's on the same page and they have an understanding of the purpose of a tool, how it's going to be implemented, what we expect to get out of it. Uh. That's actually crucial for anything, whether it's a I or not. But because I've seen so many examples of companies where you have one department who's like a business development team wanted us to put this in and I don't understand why. And if they don't understand why, then you don't get as good output on the other end of it. I think making that part of the conversation just as much as you know, determining the approach to get a trustworthy AI, I think that's absolutely crucial. Yes, And you know, a lot of times we think it's a technology problem to fix, right it's it's a technology. You know, to build trustworthy air you need to you know, it's a technology problem. It's your data scientists and engineers, which you think about it, but that's that's not the case, right, It's a it's the entire group that needs to come together. And the risk is not just from a technology perspective. It's a brand and reputation rusk. There's financial consequences, there's customer satisfaction consequences, there is so many other risks associated with if your AI is not trustworthy. Bina and I have a little bit more to talk about with AI, but before we get to that, let's take another quick break. I remember covering that over in the European Union there were various departments that were even talking about concepts that again are let science fiction far off concept, but even the concept of of granting personhood toward sufficiently advanced AI for the purposes of figuring out accountability and responsibility for when something goes wrong, who gets held accountable when the AI doesn't work right? What's your take on that. I think, you know, we might reach at that at some point, but in the interim till we don't have that kind of you know, rules or laws. I think it's absolutely you know, one of the components dimensions of trustworthy A is defining accountability upfront, meaning if the AI goes wrong, who is accountable for it? Who's going to phase the Senate hearing? Who's going to pay the fine? Is it the data scientists to milit it, is it the c I O who approved the project? Is it the CEO or is it a board member? Right? So, and the good news with that one, you know, talking about accountability upfront makes everybody proactively think about for the ways it could go wrong, because you don't want to put your name on something that might go wrong and you have not thought about it. So until we get to that, you know, machine citizens citizen rights level, I think you know, even today there is a dimension of trustworthiness which is really around defining putting in a name for who is accountable when your AI goes wrong. I agree that that's important. I have seen some of those Senate hearings with various UH tech people sitting in the sea, and I know that if I were in one of these conversations, I would not want to be that person. And making sure we specifically define who that person is and that it's not me would be top of my priority life. Well, I'm also curious then. Uh. So we've seen in a similar sense some movement on things like autonomous cars. Uh. In a similar note, I'll talking about accountability, where we're starting to see more governments try and consider who is accountable for any accidents that might have happened under cars autonomous or semi autonomous operation. Obviously that's been a big point of discussion here in the United States, and uh, this is one of those things. How how how closely tied do you think do technology experts need to be with say politicians who may not have the insight into tech, but yet are also responsible for creating and enacting policy that's going to have an effect on tech. Is do you see there being more cross talk? Yeah, you know, unlike the car example and the seed belt and speed limit example. You know, AI does need an understanding of technology so to come up with those speed limits. It is so, you know, and we've honestly entered that era where collaboration is king, right. We have to make sure that regulators and technologies, uh, policymakers, they have collaborating and each one is learning from the other. To come up with the best possible guard rails or regulations or laws, because this is not something that can be done in isolation, and like that auto speed limit example. So I think we're going to see more whether it is an entities being set up who will drive this collaboration, but there is definitely, you know, across the globe technologists being pulled together, whether as an advisory committee or a council. That is happening now, and you know, I do think we will start seeing results of that collaboration coming out sooner rather than later. I think I also believe that just like I was talking about every organization should train all their employees, I think every everybody who is involved in the regulation making process should have a basic understanding of AI, level of AI fluency, or you know, an understanding of what does machine learning really mean, what can it do, what can it not do? So I call it the AI literacy training, right, So I think it's that's like ground stakes to drive a productive collaboration. But I think this is the time for people like you and me, Jonathan, to really step up and make sure that we're collaborating closely so that that it's informed and informed and relevant regulation or relevant policy that's put together. I think relevance is is absolutely the right word to use. UH. Again, I'm not putting anyone on blast, but there have been plenty of stories of people, whether they are in the regulatory field or general politics, where their level of tech savvy is probably not even measurable based upon some of the things we've seen, and that is that is terrifying when you realize the reach and the effect of technology and how if you have a misunderstanding of it, you can tackle something that's not really a problem, but you've built it up as if it were while completely missing things that we absolutely need to pay closer attention to. So I I do try to to make literacy one of those things that I push for and hopefully I succeed more often than I fail. Yeah, it's we live in this era now that you know, at least in the UH. In the corporate world, right, we're seeing more and more boards getting more technology savvy. Leaders are leaders who understand technology so that because every company uses technology, uses AI no matter which industry they're in, right, So we're seeing that composition of boards changing, right, And I don't think we're very far from the time when you know, having a basic AI or technology understanding will be almost a prerequisite. Right Again, as I said, we're living in this interim crazy phase where there's a lot of things happening and we don't necessarily have all the foundations set up. The exciting news is for our generation, Jonathan, this is our opportunity. Right the work we do today is going to be setting the foundation for future generations. So I think, uh, you know, having that basic AI literacy, No, it's not set up, but you know, we we now understand that, you know, everybody who is involved in policymaking our regulations need to have that basic understanding. So let's make sure that you know they have that. That's great, it's it's it's looking at something that I have defined as a problem and you have defined as an opportunity, which I needed to hear honestly, because that's the kind of optimism that I find really motivating. Been a thank you so much for being on the show. Your book Trustworthy AI. I have a copy coming to me. I have not yet been able to read it. I am so eager to go cover to cover on this because just this this conversation has really energized me, and um, you know, when you have a podcast about tech and you've done more than sevent episodes, sometimes you feel like I've said everything there is to say about that, and then I have a conversation like this and I realized, this is an Iceberg situation and I've just touched the very tip of it. There an entire world beneath the surface of the water that I haven't even scratched. So thank you so much for coming onto the show, Jonathan. This is a very energizing conversation for me as well. Thank you so much for having me on your show. Once again, I have to thank Bena Amanath for coming on the show. Uh. I was thrilled at this opportunity when I first got the email suggesting that I have her on my show, because to be totally clear, her team reached out to me and I just didn't even think about that possibility. I am so glad that I followed up with that. I do plan on having more interviews on this show in the near future. I've got a couple more lined up. I'm gonna try and do that more frequently. It is I'm gonna be transparent with all of you. It is very tricky for me because scheduling UH is tricky. People are very busy, and it gives me a lot of anxiety just being absolutely transparent with all of you out there. The the the process of scheduling gives me a lot of anxiety. So it's something I'm working through and I'm trying to get more people on the show one because there's so many interesting people out there. And just with this conversation with Bena, I really got that that feeling of I need this because it is giving me more perspective than what I have and I'm I don't want tech stuff to just be a narrow laser focus of what Jonathan thinks about tech. Secondly, UM, you know, I think that it benefits the show obviously to have that that extra voice in there, and that means that it becomes more enjoyable because despite my enormous ego, I realize I cannot be the most entertaining person in all the world, UH, no matter how hard I try. So I hope you all enjoyed this. If you have suggestions for future topics, maybe you have suggestions for future guests I should try and get on the show. Reach out to me. Uh, I promise I will do my best to get that person on the show. I can't promise that it will happen, but I'll try and I'll work through this weird stress I get whenever it comes down to trying to schedule things and uh, and just to be clear, Bena was amazing because we actually tried to record that interview on one day but had a technical issue ended up having to reschedule. She was amazing. It was really good about all that. So despite all of my anxiety, everything went great, which I think is this isn't meant to be a therapy session. But I think that's very typical for me, where I get worked up about something turns out that something wasn't really that big a deal. It was just the anticipation of it that was the problem. So if any of you out there suffer from something like that, you know you have that same sort of experience. Listen, I got your back. I know how it is. It is frustrating, but you can do it all right. Eight PEP talk Over, Episode over. I hope you enjoyed it. I am on vacation for the rest of the week, so you should expect some classic episodes for the rest of this week. But that doesn't mean they're bad. It just means they're old, just like me. I'm old, but I'm not bad, and I will talk to you again. Oh if you want to reach out to me, you gotta do it on Twitter. The handle for the show is tech Stuff h s W There. Now I get to say the end catchphrase, I'll talk to you again really soon. Tech Stuff is an I Heart Radio production. For more podcasts from my Heart Radio, visit the i Heart Radio app, Apple Podcasts, or wherever you listen to your favorite shows.

Welcome to tex Stuff, a production from I Heart Radio. Hey therein Welcome to tex Stuff. I'm your host, Jonathan Strickland. I'm an executive producer with I Heart Radio. And how the tech are you? I gotta treat view folks. Today I had the opportunity to speak with Bena Amnath, the executive director of Deloitte AI Institute. Vina is an accomplished technologist and expert in artificial intelligence. She's a coder, she's, you know, an engineer, and she's a great communicator too. She has appeared on numerous shows and panels talking about AI. She's also the author of a book called Trustworthy AI. And this was a fantastic opportunity to speak with someone who actually has a deep amount of experience in the field and really talk about some of the big concepts in AI and get a little more perspective on them. And I have to admit Beena's responses really open up the blinders that I have on And of course I'm like a lot of people, right I I go through life thinking I have a pretty good handle on this. I think I know what's going on, and then I meet someone else who has had a you know, a different experience, and especially a different depth of experience in a particular field and realize, oh gosh, I hadn't even considered some of these specific scenarios, for example. So I really very much enjoyed my conversation with Bena. She's also incredibly good at putting things in a way that are easily understandable. A lot of technologists, when you start to talk with them, they get really heavy with jargon or or concepts. That makes sense if you've had experience working in that area, but if you haven't, your eyes kind of glaze over and you just trust that what they say makes sense. That was not the issue with Bina. She is really good at talking about this stuff on a level that that the average person can easily understand, and yet also really stressing how AI is a very important component today. I mean, we're seeing it rolled out in all sorts of different ways across all different sectors. We mostly talked about business in this conversation, but clearly AI is everywhere. Whether we're talking about facial recognition technology that might be built directly into the camera on your phone, or maybe we're talking about a personal digital assistant, you know, something like the Amazon one. I won't say her name because some of you have her and she gets real like she she perks up when you say her name. Um, those sort of things obviously have components of AI built into them, but we were really looking at things like processes in business where you might need to use automation and artificial intelligence to make complicated processes more efficient and less human intensive. And so, yeah, this was a great conversation and I really feel like I learned a lot. I hope that you all enjoy it. And again, she does have a new book out. It's called Trustworthy AI, and there's a copy on the way to me, so I'm very eager to read it myself because just talking with Bina I felt like I was just scratching the surface. But you're gonna hear all that, So let's get to that interview. Bina. I want to welcome you to the show. I am so pleased to have an expert on AI. Trustworthy AI, no less, Welcome to tech stuff, Jonathan, thank you so much for having me on your show. I really enjoy your episodes, so I'm looking forward to having this conversation with you. I am as well. And one of the things that I like to do is kind of set some foundation for any kind of conversation around AI, because in my experience, and I'm sure you've experienced something similar chatting with people about AI, it seems like everyone has a different, sometimes very specific idea of what AI is. And I'm curious, how do you describe AI to people? Yeah, that's a great question to start with. So AI is a form of intelligence that uses machines to do things that traditionally required human intelligence. So it is artificial intelligence which is created artificially by machines. So now I like that description because it covers such a wide spectrum every thing from sort of the science fiction approach we've all seen about machines that seem to think like humans to a point where they usually become the threat. I mean, that's typically the way we look at it, which is I'm sure going to come into play when we talk about trustworthiness, because I'm sure a lot of people aren't aware how AI can sometimes be a danger, but not necessarily like sky Net from Terminator type danger. Let me elaborate on that description than a little bit more on you know that next level down on AI definition, Right, you know, the way I think about it. There are three types of AI. One is artificial narrow intelligence, which can do a very specific, narrow task that a human can do, like sort a bunch of photographs, Right, That's a very narrow specific task. So that's artificial narrow intelligence. And then there is a form of artificial general intelligence, which is a form of AI that can do any task that human beings can do, right, So it is pretty much everything that a human being can do. And then I think of a third category, which is artificial super intelligence, which is a form of intelligence which is smarter than all human beings combined and can do more things that human intelligence couldn't do. So when we talk about AI in the business world or in reality, it is where we are with AI. It's very much in that artificial narrow intelligence space. But when we hear a lot about the in the media or the hype and the fear, you know, it's talking really about that super intelligence phase, which is a form of AI that is smarter than all human beings combined and has more capabilities that human intelligence. And I think there's a big gap between reality and between where we you know, we are anticipating things to be right and and you know. Part of the reason is, you know, actual super intelligence is a lot of our human imagination, which is where AI was when I was studying years ago, right, So I do think there is value in imagination. I do think there is value in thinking of worst case scenarios so that you can address it. But the reality is we still today don't have the tools or the capabilities to build out artificial general intelligence or super intelligence, and we do not have that capability today. I see parallels in this as well, like I have a very similar description of autonomous cars, for example, like people talk about autonomous cars like we've reached level five autonomy, when really I would argue we're still around level two creeping into level three, but we we are not close to level four or five. And this is why I like having this kind of conversation right up front, so that people kind of set their expectations, because artificial intelligence can already do incredible things in these very narrow, narrow uses, and I'm blown away by it whenever I learned about that. But I do also see the the allure and sometimes the trap of extrapolating that beyond the narrow cases and thinking what happens when this goes beyond that, which it could very well happen, but we're not we're not at that stage yet. Um. But but I think of things like, yes, I think of things like like the image recognition like that to me is still an amazing thing to see developed, Like you know it is. Ever since I started covering tech, the ability has grown so fast. Like I remember when, at least on the consumer side, you might see something that was like detecting a ace, not recognizing a face, but detecting the structure that makes a face and for a camera, And now you know that that looks like stone age technology by comparison of what we're seeing today. Yes, Jarthan, And you I know you've been cowering tech for a long time. You know you've certainly seen seen the early evolution, right, but you know, and look, when I studied computer science, I did program assembly language programming basically using zeros and ones right that level. And you know the languages that I use, like Pascal and Fortran lots, you know those don't even exist today, right, So there's a whole evolution happening. And I do think that is a big component to imagine the future so that we can at least go towards take care of the risk, and focus on all the good things that you know AI and technology can do. Right, So imagination is a good thing, but not the fear a part. And also I think I think what you said kind of is a great message to anyone who's interested in really focusing on AI. The fact that you were working in assembly so close such a low level language, you get you get a real familiarity with what these machines can do and their their potential that I think, uh, you you almost lose when you start working on high level uh programming languages Like you you get so focused on what the programming language lets you do. But if you've worked out at that low level, you're like, hey, no, I know circuits and wires, Okay, I am, I am, I am one step away from this machine. Yeah, we we have to realize, you know, just like you know, when you talk about it today, will talk mostly about the software, but the hardware is also evolving. Right. We certainly don't have wax of those massive back from pure systems that we had to program, right. I think there is evolution happening in every dimension, and you know, it's it's part of the growth of AI or any technology if you think about it. M hmm, Well, I also want to know what you mean when you when you use the phrase trustworthy AI. So what is it that makes AI trustworthy? And what what's the what's the alternative? What is the untrustworthy side? Yeah, that's that's a great question, and that's something that you know. As a technologist, I'm enamored by all the cool things that AI can do because I just focus on all the value creation. But over the past a few years, as AI started becoming real, I also realized that there are you know, with all the good things that can do, there are negative consequences to it, right, and so I put that negative consequences in the under the bucket of untrusted untrustworthiness. Ethics is a big composed under of it, right, Whether the AI is fair or biased or transparent explainable, but also things like is it compliant with local regulations, does it have controls in place? Does it have governance in place to continuously monitor for it going wrongue because you know, Jonathan, today AI is mostly machine learning, so that it's learning and evolving. It's not that era when we developed code put it out there and the code states static and its behavior was very predictable. With AI, the outputs can change depending on inputs your feed and it's impossible to trade on all possible inputs. So trustworthy for me is you know, really, or is when you have addressed, when you have thought about and addressed all the possible negative things that this AI solution can cause. Well, I would love to kind of dive into a little bit more of that because one of the things that you said that really resonated with me was the idea of transparency, because I have covered this past episodes of tech stuff, but the sort of the black box problem of creating a system, for example, a machine learning system, and you have this this machine that's training itself over and over and over. Maybe it's adversarial training, maybe you actually have two systems that are set against each other and you're training, and the issues that can arise if you have distanced yourself so far from what the machine is doing that you are unable to determine the process by which it arrives at its conclusions. And that to me is one of those those pitfalls. Yes, but I would also challenge it a little bit, Jonathan, because that a whole synthesis of my book is that it depends on the use case. It is not a one size fit soul. So depending on where and to solve what problem are you using that DAIR solution, it is for that organization that teams decide if transparency is crucial. Right. If if your AI solution is being used to for patient care in a hospital system, then transparency is absolutely crucial, right. But if you are using the AI solution to predict when an X ray machine might fail, and you're able to predict at accuracy rate that this machine is going to fail in the next forty eight hours of call a technician, transparency may not be as crucial, right. So I think transparency is crucial depending on the use case. And that's true for all the other dimensions as well, even fairness and bias, which we hear a lot about. So it really depends on the use case that that you're using the I fall Hey there, Jonathan back at the home studio, just here to say we are going to take a quick break, but we'll be back with more with Bena Amanath, the executive director of Deloitte AI Institute. Bias doesn't necessarily mean negative, depending upon the use case of the technology. In some cases you need to have a biased system because it's specifically meant to be weighted to do one thing versus another, and without the bias it doesn't do that. But the way we typically hear about bias is when it is making a negative impact, when it's something for it's like like the facial recognition technologies. We've heard plenty about that. So it is interesting to me, And uh, I'm curious, like, what are what are some of the uses of AI you're seeing in technology now that you find really exciting. Yeah, no, I think you know, we're still very early on in this technology evolution and there are still so many use cases to be solved, so many industries to take a I too right to point about bias and its relevance. I completely agree with you that, you know, it depends on the use case, and it goes back to that first question we talked about, right, how AI is really emulating human intelligence, which means that it is going to carry over the biases of the humans that are building it. Right, But as as a business or as an organization who's looking to use an air solution, who's looking to develop an a solution, they really have to, you know, bring together the stakeholders to discuss and decide how crucial is fairness or unbiased ness important in this particular AI use case. And easy one out is if it doesn't involve human data, then you probably don't have to worry as biased as a factor and address it. And if it does involve human data, then again there is weightage in what right if there is biased at in an algorithm that is providing personalized marketing, that you know that there is a weight to it. And if it is if there is biased in an algorithm that is supporting law enforcement decisions, that's a higher rate, right. And it's really about rating it, you know, weighing it and deciding which ones are the one where biases acceptable and you can still proceed and get value from the AI solution, and which are the ones where it is absolutely not acceptable and you need to stop and figure out and alternate way to solve for that problem. It's fascinating because it to me this is starting to sound and I agree with you, like the machines we build are in large part reflections upon ourselves, especially when we're talking about coding and software. I mean obviously that's going that's a creative process. I don't know that everybody views it that way, but I think of it very similar to creating any kind of creative work. It's a reflection of your process and your you know, the things that are important to you, the things you've prioritized. And it makes me think of how we're in an era now where I'm getting a little in the weeds here, but we're in an era where we're more likely to address things like, uh, mental health and the fact that we need to be mindful and we need to improve ourselves. And it's almost like taking that same approach, but applying that sort of thinking to designing a system so that we are being mindful to create the best system for whatever purpose it is it's intended to address. Yeah, you've got it exactly right. The way I think about it is, how can we reduce the unintended consequences? Right? We know there are going to be risk associated with it, How are we going to have a discussion prior to putting the solution out into the world and then you know, see all the negative impacts. Can we have a proactive discussion as part of your project planning meeting or your design meeting right to proactively identify what are the ways this could go wrong and fix it. Johnathan, you know, the easiest example that I can give is we're living in this very interesting era where you know, AI as a core technology is developing and you know, there are all these the value that you're getting from it, and then there are all these negative things that can happen. So think about you know, way back when when you know the cars were first invented, right, we didn't even have proper roads. We didn't have seed belts, we didn't have speed limits, right, and be in that phase where there are cars running on the road. They're taking us from point to point be faster, so we want to use it, but we don't have the seat belts put in place, we don't have the speed limits set in place, so you're going to see accidents. But we are humans. We're going to learn from it and we're going to come up with those speed limits. We're going to figure out what are those card rails, and it is going you know, we are going to you know, achieve a point where you know, we have those guard rails in place so that you can run with AI faster. It's just that this interim phase is when you know, we have to figure it out out in tandem while it's running in the real world, causing accidents. And in some cases that's that those accidents can be things where you have it maybe in a test environment and you think, oh, this isn't behaving the way I thought it was. But you know, thank goodness, it hasn't been deployed out in the real world for or within your company's UH processes, so you think, oh, well, it didn't wipe out all of our revenue because it's in a test environment. UH. And in other cases, I see I see some companies. I'm not gonna name names, Bina, I'm not gonna put anyone on blast here, but I have seen some companies that have taken that kind of idea and applied it in UH specific deployments of technology where there can have some some real world negative consequences to end users. UM. And that to me has always a concern I find, yeah, I find that I find it hits me wrong. Yes, And that's the reality of how we've evolved as a technology in their technology space. It's a bunch of you know, technologists coming together and building these cool, new shiny technologists. Look. You know, as I said, I am a technologist in my DNA my training, and it's very easy to just focus on all the good things that can do. But with AI, now that realization has hit, you need other you know, skill sets at the table, whether it is a social site, is philosophers, legal and compliance to help us figure out those seedbells and the you know, the speed the lanes, you know, because technologies by themselves cannot do it. So you'll see more of the discussions coming around ethics and which is resulting in new roles and new jobs, which becomes core and part of your engineering process. Right, So that scope of who is involved in designing and developing AI is definitely increasing. And the other big part, you know, and this has been a challenge since I started in tech. You know, there's a lack of diversity in tech. It's a reality, right, But unfortunately, because AI is so closely tied to human intelligence, if you don't have enough diversity from you know, not only from a gender, race at necessity perspective, but even a diversity of thought, right, you're the AI solution you built is not going to be as robust as it could be if you had a diverse team at the table. Right, you've probably heard of that classic example of you know, the robotic vacuums, right, how it was designed and now it was built out. And then in the Eastern cultures it's normal to sleep on the floor and it sucked up human somebody who's sleeping their hair because it was never trade on it. It didn't come you know, it didn't come to the discussion, and it was being designed because nobody was there from that culture. Right. So I think, you know, the realization that you need more diversity at the table, you need more controls in place. It's all coming to the forefront. I definitely see companies addressing it. But the the DNA will now has been oh, look at all the cool things this technology can do, let's go put it out right. But I do think, you know, companies are getting mindful about it and hopefully we'll reduce the number of unintended consequences. Yeah. I see the same thing reflected in the open source community, where you have an open source approach to developing software, and because it's open and and anyone interested and capable can contribute ideas get tested, very quickly. New new perspectives get incorporated very quickly. Things that are working stick around, things that don't work get improved. And the way I've described it to other people is, if you have a closed off garden that you're working on, you're only as good as the smart people who happen to work for you. And if you go with this other approach where you purposefully open it up, which is like the biggest version of let's let's try and get as much diversity of thought in here as possible. Uh, you don't have that limitation because you've You've just said, well, now the world is I mean it's not the whole world, but but effectively the world. The world can contribute if if they, if they wish, and uh agree, I think having that diversity is absolutely key to creating solutions that work for as many people and as many potential uses of that technology as possible. I being a a a white man in the United States, I am I am essentially the catered to audience for a lot of tech, and so I've seen how things that were made to work really well for me do not work for some other people. And that's such a tiny little microcosm when we're looking at you know, the GREA and scope of tech which goes so far beyond just consumer electronics. UM I absolutely agree that that diversity is is required if we're going to have a i that is truly trustworthy. Yeah, exactly, And you know, and then but there it's not never. It's never as straightforward as we tend to simplify it down to, right, like when we talk about explainability. There there are real challenges and those are real business challenges on even when you go down the open source route right, a lot of time, if you go too much on the explainableitypath, you know, you you have to still share data and algorithms and those are strategic assets and it can result in compromising your company's i P. Right, it can result in you know, security hacks because the more explainable you make it, it is more susceptible to manipulation if it's functionality is fully understood, the privacy aspect of it, prioritizing playability and you know, how do you make sure you hit a balance of why you are making sure you're mitigating the risk but at the same time protect your organizational i P. That's that's a that's a solution. That's that there is no one single answer. It is for the stakeholders to come together and discuss it and identify were that balances, because it's going to be different depending on your business. It seems to me like you're saying the real world is a complicated place and there's a lot of different shades of complexity to it, and that I can't just simply uh summarize it in a black and white approach, which I greatly appreciate, uh, and that that's interesting to me too. I'm glad to have that perspective because again, like as a as a communicator for tech, uh, I know that I too fall into the same sort of pitfalls of oversimplifying for the purposes of trying to get a concept across, because to really dive into it, you start to you start to feel like they're there are so many threads that you can't see the rope and that or you can't see the forest for the trees if you prefer. But but that's that's very important to remember, and I think it is a great reminder that again, like we said at the top, that the use for this technology kind of defines the approach that you need to take in order to make certain that you're you're getting the result that you want. UM from a really high level, can you kind of talk about your concept of what what it is? This is almost a trick question because there's so many different variations, but what what what an organization's process would be when considering to implement AI solutions like high high level approach. Yes, Historically it's always been you know, how can we use ARE to solve this business problem? And what's the r O I what you know? How much profits are we going to increase by doing this? Or how much costs are we going to save by doing this? Trust me, I've done this project and you know that's how you know every conversation starts because we want to make use technology to drive more business value, right, whether it is through customer engagement, optimizing our existing process and so on. I think the discussion that that if you are serious about getting making your AI trustworthy, the discussion that needs to happen upfront is defining what does trustworthy I mean for for my organization? Right? And uh and it could be different depending on the organization, It could be different depending on the use case. But having those high level principles, and there are plenty of principles out there, there are plenty of frameworks out there, but I think every organization needs to think about what are the key pillars that they agree upon and that they would never want to void it right. And once you have those, then next step is to decide to make sure every employee within your organization understands it. Because it's not just your I T team, It's not just the engineers of the data scientists who need to understand ethics. It's that marketing marketing account person who is looking at using an AI solution, buying it from a vendor to use it within your company. They need to make sure that they are asking the questions which ensure trustworthiness and do they do is the software they're buying, has it been tested for fairness? What was it tested for? So every employee within the organization needs to understand what distrustworthy I mean for my company and how do I how do I make it, how do I use it in my role? So role specific training. And then the other crucial factor to decide, and we've see variations of it in the industry, is you know whether it is getting a cheap AI Ethics officer or setting up an AI thinks advisory board right, making sure that there is somebody who is responsible to keep you know, to keep this moing within the organization is super important. That's more from a people perspective. And then the last thing is really looking at your existing processes. I don't think you need to completely come up with new processes or new controls, but just adding in an trustworthy check in your existing engineering processes or in your existing development process or your procurement process to make sure you're checking for the trustworthiness of any AI that tool that you buy or that you build, you know, having in addition to the r O, I ask Sen spent ten percent of your time to brainstorm on what are the ways this could go wrong? Right? And capture it and when you build that technology, put those guard rails in place. Now it is guaranteed you It is impossible to identify all the possible ways it could go wrong, but even if you get you know the ways it could go wrong, it is better than not thinking about it and not addressing it. So that is a very comprehensive way you can do it. But it is all easy. It fits in with the existing trainings and processes that you already have in your business. Right. I gotta say, like as as someone who is a technologist and uh and coming at this from that angle, that was such a human centric kind of answer. I really appreciate that. I've had a lot of discussions with various leadership around different companies and this idea of of having that explanation and getting buy in from different departments so that everyone's on the same page and they have an understanding of the purpose of a tool, how it's going to be implemented, what we expect to get out of it. Uh. That's actually crucial for anything, whether it's a I or not. But because I've seen so many examples of companies where you have one department who's like a business development team wanted us to put this in and I don't understand why. And if they don't understand why, then you don't get as good output on the other end of it. I think making that part of the conversation just as much as you know, determining the approach to get a trustworthy AI, I think that's absolutely crucial. Yes, And you know, a lot of times we think it's a technology problem to fix, right it's it's a technology. You know, to build trustworthy air you need to you know, it's a technology problem. It's your data scientists and engineers, which you think about it, but that's that's not the case, right, It's a it's the entire group that needs to come together. And the risk is not just from a technology perspective. It's a brand and reputation rusk. There's financial consequences, there's customer satisfaction consequences, there is so many other risks associated with if your AI is not trustworthy. Bina and I have a little bit more to talk about with AI, but before we get to that, let's take another quick break. I remember covering that over in the European Union there were various departments that were even talking about concepts that again are let science fiction far off concept, but even the concept of of granting personhood toward sufficiently advanced AI for the purposes of figuring out accountability and responsibility for when something goes wrong, who gets held accountable when the AI doesn't work right? What's your take on that. I think, you know, we might reach at that at some point, but in the interim till we don't have that kind of you know, rules or laws. I think it's absolutely you know, one of the components dimensions of trustworthy A is defining accountability upfront, meaning if the AI goes wrong, who is accountable for it? Who's going to phase the Senate hearing? Who's going to pay the fine? Is it the data scientists to milit it, is it the c I O who approved the project? Is it the CEO or is it a board member? Right? So, and the good news with that one, you know, talking about accountability upfront makes everybody proactively think about for the ways it could go wrong, because you don't want to put your name on something that might go wrong and you have not thought about it. So until we get to that, you know, machine citizens citizen rights level, I think you know, even today there is a dimension of trustworthiness which is really around defining putting in a name for who is accountable when your AI goes wrong. I agree that that's important. I have seen some of those Senate hearings with various UH tech people sitting in the sea, and I know that if I were in one of these conversations, I would not want to be that person. And making sure we specifically define who that person is and that it's not me would be top of my priority life. Well, I'm also curious then. Uh. So we've seen in a similar sense some movement on things like autonomous cars. Uh. In a similar note, I'll talking about accountability, where we're starting to see more governments try and consider who is accountable for any accidents that might have happened under cars autonomous or semi autonomous operation. Obviously that's been a big point of discussion here in the United States, and uh, this is one of those things. How how how closely tied do you think do technology experts need to be with say politicians who may not have the insight into tech, but yet are also responsible for creating and enacting policy that's going to have an effect on tech. Is do you see there being more cross talk? Yeah, you know, unlike the car example and the seed belt and speed limit example. You know, AI does need an understanding of technology so to come up with those speed limits. It is so, you know, and we've honestly entered that era where collaboration is king, right. We have to make sure that regulators and technologies, uh, policymakers, they have collaborating and each one is learning from the other. To come up with the best possible guard rails or regulations or laws, because this is not something that can be done in isolation, and like that auto speed limit example. So I think we're going to see more whether it is an entities being set up who will drive this collaboration, but there is definitely, you know, across the globe technologists being pulled together, whether as an advisory committee or a council. That is happening now, and you know, I do think we will start seeing results of that collaboration coming out sooner rather than later. I think I also believe that just like I was talking about every organization should train all their employees, I think every everybody who is involved in the regulation making process should have a basic understanding of AI, level of AI fluency, or you know, an understanding of what does machine learning really mean, what can it do, what can it not do? So I call it the AI literacy training, right, So I think it's that's like ground stakes to drive a productive collaboration. But I think this is the time for people like you and me, Jonathan, to really step up and make sure that we're collaborating closely so that that it's informed and informed and relevant regulation or relevant policy that's put together. I think relevance is is absolutely the right word to use. UH. Again, I'm not putting anyone on blast, but there have been plenty of stories of people, whether they are in the regulatory field or general politics, where their level of tech savvy is probably not even measurable based upon some of the things we've seen, and that is that is terrifying when you realize the reach and the effect of technology and how if you have a misunderstanding of it, you can tackle something that's not really a problem, but you've built it up as if it were while completely missing things that we absolutely need to pay closer attention to. So I I do try to to make literacy one of those things that I push for and hopefully I succeed more often than I fail. Yeah, it's we live in this era now that you know, at least in the UH. In the corporate world, right, we're seeing more and more boards getting more technology savvy. Leaders are leaders who understand technology so that because every company uses technology, uses AI no matter which industry they're in, right, So we're seeing that composition of boards changing, right, And I don't think we're very far from the time when you know, having a basic AI or technology understanding will be almost a prerequisite. Right Again, as I said, we're living in this interim crazy phase where there's a lot of things happening and we don't necessarily have all the foundations set up. The exciting news is for our generation, Jonathan, this is our opportunity. Right the work we do today is going to be setting the foundation for future generations. So I think, uh, you know, having that basic AI literacy, No, it's not set up, but you know, we we now understand that, you know, everybody who is involved in policymaking our regulations need to have that basic understanding. So let's make sure that you know they have that. That's great, it's it's it's looking at something that I have defined as a problem and you have defined as an opportunity, which I needed to hear honestly, because that's the kind of optimism that I find really motivating. Been a thank you so much for being on the show. Your book Trustworthy AI. I have a copy coming to me. I have not yet been able to read it. I am so eager to go cover to cover on this because just this this conversation has really energized me, and um, you know, when you have a podcast about tech and you've done more than sevent episodes, sometimes you feel like I've said everything there is to say about that, and then I have a conversation like this and I realized, this is an Iceberg situation and I've just touched the very tip of it. There an entire world beneath the surface of the water that I haven't even scratched. So thank you so much for coming onto the show, Jonathan. This is a very energizing conversation for me as well. Thank you so much for having me on your show. Once again, I have to thank Bena Amanath for coming on the show. Uh. I was thrilled at this opportunity when I first got the email suggesting that I have her on my show, because to be totally clear, her team reached out to me and I just didn't even think about that possibility. I am so glad that I followed up with that. I do plan on having more interviews on this show in the near future. I've got a couple more lined up. I'm gonna try and do that more frequently. It is I'm gonna be transparent with all of you. It is very tricky for me because scheduling UH is tricky. People are very busy, and it gives me a lot of anxiety just being absolutely transparent with all of you out there. The the the process of scheduling gives me a lot of anxiety. So it's something I'm working through and I'm trying to get more people on the show one because there's so many interesting people out there. And just with this conversation with Bena, I really got that that feeling of I need this because it is giving me more perspective than what I have and I'm I don't want tech stuff to just be a narrow laser focus of what Jonathan thinks about tech. Secondly, UM, you know, I think that it benefits the show obviously to have that that extra voice in there, and that means that it becomes more enjoyable because despite my enormous ego, I realize I cannot be the most entertaining person in all the world, UH, no matter how hard I try. So I hope you all enjoyed this. If you have suggestions for future topics, maybe you have suggestions for future guests I should try and get on the show. Reach out to me. Uh, I promise I will do my best to get that person on the show. I can't promise that it will happen, but I'll try and I'll work through this weird stress I get whenever it comes down to trying to schedule things and uh, and just to be clear, Bena was amazing because we actually tried to record that interview on one day but had a technical issue ended up having to reschedule. She was amazing. It was really good about all that. So despite all of my anxiety, everything went great, which I think is this isn't meant to be a therapy session. But I think that's very typical for me, where I get worked up about something turns out that something wasn't really that big a deal. It was just the anticipation of it that was the problem. So if any of you out there suffer from something like that, you know you have that same sort of experience. Listen, I got your back. I know how it is. It is frustrating, but you can do it all right. Eight PEP talk Over, Episode over. I hope you enjoyed it. I am on vacation for the rest of the week, so you should expect some classic episodes for the rest of this week. But that doesn't mean they're bad. It just means they're old, just like me. I'm old, but I'm not bad, and I will talk to you again. Oh if you want to reach out to me, you gotta do it on Twitter. The handle for the show is tech Stuff h s W There. Now I get to say the end catchphrase, I'll talk to you again really soon. Tech Stuff is an I Heart Radio production. For more podcasts from my Heart Radio, visit the i Heart Radio app, Apple Podcasts, or wherever you listen to your favorite shows.