EP 224: AI and its Impact on Society: How it might look

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Artificial intelligence is going to have a tremendous impact on society. It already has, right? We've seen over the last year and a half this Gen AI boom, change the way the lot of us even work, the way a lot of us even function. So I'm extremely excited to be talking about that today on today's episode of Everyday AI. So welcome. My name is Jordan Wilson and I'm the host of Everyday AI. We're a daily livestream, podcast and free daily newsletter helping everyday people like you and me not just learn what's going on in the world of Gen, but how we can all actually leverage it, right? And it doesn't mean anything if we can keep up with the news and the tools and the techniques, if we can't ethically and responsibly use it to grow our companies and to grow our careers. So that's what Everyday AI is all about. And we talk to people from all over the world. And you know, that's one of the challenges of being a live stream, right? Is sometimes there's people from other time zones that we can't always bring on live. So that is today's. So if you're listening to this, yes, we are debuting it live, but it was technically pre recorded. But with that, don't worry, I'll probably still be in the, in the comments answering questions. So if you have any questions, feel free to still ask them. But with that, I'm extremely excited to bring on our guest for today's show. So please help me in welcoming James Hodson, the CEO of AI for Good Foundation. James, thank you so much for joining us.

Thank you, Jordan. It's a pleasure, real pleasure to be here.

All right, can you tell us a little bit about, you know, not just the AI for Good foundation, but obviously, you know, what you do in your role heading the foundation.

Absolutely. And I guess, you know, just to head it off, I mean, the nice thing about it being a pre recorded show is I don't need to worry about, you know, saying the wrong thing. So it's all good. Right? So AI for Good foundation is about 10 years old. It's a non profit based out of California, but operating around the world as a nonprofit. Our mission has been and is today economic and community resilience through technology. We look at the world through the lens of technology and AI in particular, as you can imagine from our name and we try to think about how we can strengthen that society, how we can increase the resilience of that society through difficult, complex situations. And we help governments, private entities, NGOs around the world to be able to create, plan, deploy and operate infrastructure that uses advanced technologies in order to kind of build those systems and improve those outcomes. That means operating in disaster areas. So it means operating in places like Ukraine. It means operating in countries like Ethiopia that are going through massive economic development and technological changes. It also means operating here at home in the us, in Canada, in Europe, in order to improve the ways that governments interact with technology and the way that companies use technology.

Tell us just a little bit. So I mean, you already mentioned international reach, trying to use artificial intelligence in a positive way, but can you just tell us a little bit about the size of the organization? Know, I follow your guys work, but maybe a lot of people don't, but you have a large reach, you know, both people on your team, board members, et cetera. So can you just talk a little bit about even just kind of bringing people from, you know, highly visible parts of the world, you know, business leaders into the fold and how just this AI for good even works, you know, as a, as a mechanism for using AI for good?

That's a fantastic and fair question, I would say. So we have two aspects to how we operate, right? One is of course, we're building. We're out in the communities we're building, we're developing technologies and we're making sure those technologies are responsive to human needs. We talk about human first technology. The other side is we are a bit of a global advocacy network. We were born out of the academic research community and the economic and policy making community. And as a result of that, we have a fairly big reach around the world, as you mentioned. Now the, the organization itself is around 70 full time staff around the world. And that staff kind of shifts around as we, as we build stuff. We're often referred to as kind of a SWAT team. SWAT team that comes in, helps governments, helps organizations to change and then kind of builds that capacity, right, to do good, but doesn't. We don't try to stick around forever. Right. We realize as a nonprofit, our mission is to experiment, to find the way forward and then to hand off that capability to others who can scale it. So it's not ever been our aim to become, you know, a 10,000 person organization and to be, to be massive, but it is to get into those situations where there are difficult problems to solve with the right people, with the right way of thinking about it. And that's why we talk about economics and technology coming together and therefore, you know, knowing. Knowing our place and creating that innovation potential.

You know, I love, I love how you said that. Almost like, like a SWAT team for good. Right? Um, and you know, even my own background, I spent almost 10 years working at a nonprofit, you know, all over, at least here in the US and yeah, like, when you start in with your mission, you always hope that you aren't needed, right? You. You hope that you can go in and solve problems and you don't have to have, you know, a permanent presence. Right. But what is, what does it look like right now that, you know, if your mission is successful? Right. So maybe you can either walk us through a simple use case, but what does it look like to implement AI for good, whether it's in a community, a city, a country? Like, what does that tactically look like and what's the impact kind of, you know, after or, you know, what are the results that, you know, you're kind of able to show for your work in these areas?

Okay, we have a variety of programs around the world, right? And I think it each time we go in, we're not looking at the same impact metrics. Each time we go in, we're not thinking about it in a templated way. But I'll give you a few anecdotes that can maybe paint the picture of how we go about doing our work. I mentioned Ethiopia before. So we were in the privileged position of being able to work with a series of global organizations, including Tony Blair Institute out of London from 2018 through 2020, in order to build the economic strategy for Ethiopia that would basically transform the way they digitize their society and use technology. But it included also thinking about mega infrastructure projects. So, for example, in Ethiopia, there's the largest hydroelectric structure in Africa that's being completed. It's called the Grand Ethiopian Renaissance Dam. Caused a bit of a geopolitical mix up with Egypt, as you can imagine, because of it being on the Nile. But the potential of those massive infrastructure projects, when you think about kind of today's data as currency, right, and electricity being kind of the marginal provider of innovation potential within many of these contexts, rather than labor costs, as we may have seen historically. What that means is that countries like Ethiopia find themselves in a position where economic development can actually hop, right, make a big, big jump forward, versus other countries that maybe aren't going through such deep transformation. And technology in those senses can be a transformative vector. So There is designing policy, but then there's also creating technology. So we have, for example, in Ukraine, we operate a platform, a platform called Lifeforce. And Life Force essentially takes everything where government ends and provides a social safety net for people to be able to interact in their communities, to get everything that they need in real time. And that's intermediated by artificial intelligence. Right, but it's a human centric platform. It's about connecting people at the right moment to get the things that they need to do to fulfill needs and to be able to manage resources effectively and optimize that within the social context. So just to give you two anecdotes, and obviously we can go into much more detail than that.

You know, I think a lot of people, when they hear a nonprofit that does work all over the world, I think the first thing that pops into a lot of people's head is, you know, education or access to clean water, you know, eradicating disease, etc. Should we be looking at artificial intelligence? Not in that same path, because I know it's not an apples to apples comparison, but is there also this concept or this thought in your organization where the rest of the world needs AI for economic development in the same way they may need access to clean water or access to high quality education? Is that a train of thought that the organization holds or is that just me, you know, kind of with. With some random thoughts?

No, I think that's actually a really good way of explaining, of explaining it. So, kind of two quick responses to that. The first one is absolutely right. A core tenet of what we do is building capacity to use technologies within the communities that we serve. So it's not my aim to be colonial about the use of technology. Right. Despite the British accent, it's my aim to bring communities the ability, right, to build and deploy those technologies that they need to solve the problems that they understand for themselves. And that's why we always build the technology with our communities involved. And it's why we always hand off those technologies to the communities to be able to run them and operate them themselves. And. And the policies have to also follow that same template. The other aspect of this is that with any kind of advanced technology, you of course, have great potential for change and to scale that change quickly. Now, what I've learned in the nonprofit sector over the last 10 years, and I'd say that I still hold very much a private sector point of view, and I think about these topics, and I think I hold quite a kind of economically driven view of how we should build these solutions is that humanitarian aid, in kind of the classical sense of the word, is actually what's been holding us back in our response to major crises around the world. And I see it continuously being a stumbling block in our ability to actually build resilience in the communities that are having the toughest challenges today. So actually, as an organization, we're pushing back quite hard against aid being the cornerstone of response, towards building the resilience within the communities so that the communities can drive their own response, select what the best way to react is, not have an economic fallout as a result of disasters to the extent possible, and therefore use technology as a stabilizing strata within that response. It's a very different way of thinking. But, you know, we could go on for hours and hours talking about humanitarian aid. And obviously the Ukrainian context, you know, to put it lightly and shortly, I would say that a vast amount of money has been wasted by Western NGOs in Ukraine before that money ever even reached Ukraine. And the things that they did in Ukraine are well understood by the local communities to not have been useful within the Ukrainian context. Anything from Red Cross handing out Coca Cola to IRC not actually spending the bulk of its money in Ukraine. I would say that we have an opportunity with technology to really rethink a lot of the ways in which we get involved around the world. Being a US based organization, we also have to be very mindful of the reputation that the US has getting involved in places around the world. And I think that being community driven and community first is a big step forward from where we have been in the past.

James, one thing I wanted to dive into a little bit deeper there is you talked about how you can using this technology, using artificial intelligence as a stabilizing force. But I think there's different ways that you can view artificial. Artificial intelligence, right? There's obviously, I think, the tremendous upside and the optimism around how it can change communities and in cities and countries. But then there's, you know, the other side too. So can we talk a little bit about, you know, what you're. Whether you want to talk personally or through the, through the lens of, you know, AI for good. But when we talk about the impact of artificial intelligence on the labor market, when we talk about, you know, I think everyone, regardless of where you are, has a healthy either respect or an outlook on, okay, will AI come and disrupt our community, our country, or even our personal jobs? What's your thought on that? And kind of the balance between using it as a stabilizing force versus it can be economically disruptive.

So yeah, that's again, it's a great question and we could dive into it headfirst and never come out again. What I will start with is I'll say that we are living through a data driven revolution in terms of how we operate as society. And that's visible through every facet of our lives, right? It's visible from the way that governments run political campaigns. It's visible from the way we do work, right? We work a lot more digitally natively today than we did even 10 years ago, 20 years ago. The level of computation availability in society is massive compared to, you know, five years ago, 10 years ago. And that's propelling a lot of the advances in algorithmic capabilities and it's allowing all the cool stuff that we're seeing, right, with these large language models and transformer based technologies in society. It's also driving a lot of worries about what it means to be able to use data, right, like it's tap water. And it's going to, I think, lead to a lot of reckoning in our society about what we really want that future to look like as a data driven future, right? We have risks. We have risks such as the risks of identity theft, right? And ultimately identity theft risk means deep fakes. It means kind of the whole gamut of issues that come with that. We have risks when it comes to targeting, right? Being able to very, very quickly identify how people are, how they act, what their likely behaviors will be and how we can influence them. And you know, I would, I would say that these are not new challenges in society, but they're certainly challenges which we are going to exacerbate to the last possible mile. And we need to learn kind of how we want to deal with that. We need to come up with policy that affects the dynamics. And ultimately, I think policy is about dynamics, the dynamics of the situation and the incentives that we want. So the policies that we put in place may not be different from what we would put in place without AI. However, AI is forcing us to really consider what we want as society in a way that we could kind of kick the can down the road before. Scale is not the issue so much as the dynamics that we're trying to attack with the policy and with, with our decisions to look at the labor market quickly, because you mentioned the labor market, and I don't want to kind of leave that unaddressed completely. But together with my colleagues on more of the academic side, economic and kind of AI side, we did publish last year A very detailed, and what I think is probably the flagship paper at the moment is the academic community that kind of understands and looks at the dynamics of AI in industry and in society. And right now what we're still seeing is the dynamics are very favorable to human labor, right? We're yet to see a big shift in the structure of human labor because of technology. However, what we are starting to see is that humans are adapting, and as I mentioned, it's creeping into every aspect of our work, but humans are adapting. Governments are starting to build very, very good reskilling programs in certain areas, not everywhere. It is unlikely that we're going to see a hollowing out of the labor market because of technology anytime soon. But we will see sectoral refocusing. We will see large swathes of certain sectors quickly shifting to, to a technology enabled stance. And that will require that we invest heavily in giving people new opportunities. It will require that we invest heavily in education. Right. And that we get our high schoolers and our university graduates prepared for a future which will be even more heavily technologically focused than it has been in the last decade. Now, if we can't do that in the U.S. and if we can't do that in Europe, then where would we be able to do it again? I'm not trying to take a western stance necessarily for this podcast, but I'm guessing most of the audience is in the US and Canada and maybe in Western Europe. And we have the innovation capacity to take care of these challenges. These are not challenges that are insurmountable, but we just need to be mindful and make sure that we're being proactive in addressing them.

And I think the thing that I'm always even thinking about personally is the dynamics. The dynamics, I think are sometimes hard to understand and hard times to balance. Because, yes, like I'd say the majority of our audience listening is, you know, from the U.S. but then, yes, we do have, you know, big audiences in other countries, you know, specifically, you know, in Europe that maybe have a different outlook toward technology. But, you know, one thing James, I wanted to, you know, dive into a little deeper is, you know, when, when, when I asked about, you know, the labor market, you did bring up policy. Right. And I know this is, this is difficult, right, because you all do work throughout, throughout the world. But at what point should that, you know, policy or government overlap with our jobs, with protecting the labor market? Right. So I think the eu, you know, with their AI act has been a little more, I guess, protective of the labor market. One could Say, or maybe not. And then in the US it's been a much more hands off approach, at least from a legislative perspective, you know, really with only an executive order and some informal governing. So what's, what's your take on kind of finding that balance, Right, between this, this, you know. Yes, this Western US free market enterprise versus protecting the labor market? Because, yeah, AI can be disruptive.

Well, the role of government in this is to smooth the transition. Right. And ensure that the right resources are in place so that it causes kind of net positive outcomes for society. Right. So you can have multiple views on what's going to work best in order to achieve that outcome. Right. You can believe that the market will get there by itself. Right. And the companies operating will know where to invest in order to kind of create the resources that they need and ultimately shift the playing field in the direction that's right. Or you can believe that you need to be a bit more protectionist towards people so that you don't have kind of too much of a jarring shift in employment. Now, Europe has always taken the approach of more protections for employees. And so it's not surprising at all that there would be a similar kind of dynamic playing out now and that Europe would be leading in trying to legislate and regulate certain aspects of the rise of technology within the labor market and within our economy. What's going to work best? That depends a lot upon kind of how consumers react and how employees react and on the pace of technological shift. And I think it's not very useful to make predictions about that. But what I would say is it's actually very interesting and probably useful to be able to see both approaches play out, at least in the short term. And I think the US is receptive also to seeing what's working around the world and to adopting and adapting to that reality as it takes place. Right. We're seeing a very interesting set of arguments taking place now around copyright law in the U.S. right. And the U.S. pioneered effective copyright protection. Right. When it comes to industrial revolution. And kind of that shaped a lot of American innovation potential over the decades, kind of next 200 years. So it wouldn't surprise me at all for the US to adopt some level of regulation that still can promote innovation over the long term. Now, remember, sometimes protectionism like copyright law is actually there to encourage innovation, not to stifle it or to slow it down. So we shouldn't always think about regulation as something that is necessarily a, a bottle stop. Right. Sometimes it can be a way of creating and Ultimately accelerating a more structured process.

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Is there any concern, you know, especially even if we were just to draw the, the parallel, you know, between us and, and, and the eu, is there concern, you know, is that just two just starkly different approaches, what that could mean for the rest of the world? Right, because I'm, I'm, I'm very interested, you know, with, you know, AI for good. You all have been around for, you know, 10 years, right, or, you know, almost 10 years, and I think the conversation is changing around AI. So, you know, now that you do have, it seems like all of this momentum, you know, happening now, is there concern with, with two just such different approaches and what that might mean for, for everyone else?

Well, you know, to take a bland and silly example, but just to kind of open up the conversation, the US and Italy have completely different regulation as to cheese imports. However, the fact that these two big cheese economies do very different things when it comes to which cheeses come into the country and what the tariff structure looks like doesn't actually cause massive problems on a global economic level. I would say that each country and each bloc in each region is going to set economic policy according to the behaviors and the expectations of the actors, the entities that are acting economically within that sphere. The US is taking a very different tack. Britain is taking a very different tack. The EU is taking a different tack. And by the way, African Union and South American countries are also going in their own direction. Japan has a very interesting set of approaches to Regulation of AI, which are being drafted now, does that mean they're going to come to loggerheads? Not necessarily. Right. However, it is an opportunity to learn, as I said before. Now one thing I will mention quickly while we're at it, because we're on very close to this topic, is we launched last year, what I think is the only nonprofit driven AI audit program for the private sector. Now why would we do that? Right. Well, first of all, it's to help organizations to develop algorithms that take into account kind of a human centric approach. And also for those organizations to be able to say, and actually have done a lot of thinking about how their technology, their data use impacts their stakeholders and the people who use their products. The second part of it is actually that we want to help organizations to figure out what type of regulations, what type of guardrails would actually be useful in different contexts and by that create essentially a wealth of information at the intersection of companies and the government to be able to have a reasonable conversation about what we should do going forward as a united set of stakeholders that are all interested in maximizing innovation. Right. Because we want the US Economy to be strong. We want, we want to invent, we want to create jobs, we want to create new companies and new ways of doing things, but we also want to do it with the maximum number of wealthy, well educated consumers, which is going to maximize our economic potential over the long term and give the next generation the best chance at living high quality lives. And so you can see this as being our attempt at getting at that wedge in between government and the private sector so that we can act as a conduit for, you know, putting in reasonable legislation if legislation is necessary. Right. And the if is, is really what we're, what we're after. Like what. And if, should we be, should we. When, when should we be getting involved and how. Right, that's the, the question.

Yeah. And I think, I think it's something I'm even thinking a lot more, right? Like, I talk to experts literally every day I read about AI and I think now more than ever I have my, you know, my, my eyes and my focus set on, okay, what happens when this happens? What happens when AI is maybe more powerful than we think. Right. And not even talking about, you know, AGI, but you know, what happens when we start to see agents. Right. Because that's what a lot of companies now are working on. And, and what happens when we do start to see, you know, kind of what, what we were talking about earlier, you know, when you Know, reskilling in certain organizations or sectoral refocusing, like you mentioned, what, what kind of safety nets should, should we be building? I mean, should we be having conversations about universal basic, you know, universal basic income? Should we be having conversations around, know, government assistance for, you know, affected areas? Where, where should the conversation go on that? And you know, is that a private, is it a, is it a public, is it a government, like, who should be involved in those conversations?

So I think that most of the people who have been pioneering discussions about what the right economic shifts should be because of AI are not qualified really as economists to be thinking about the dynamics of the economic system that we're in. And I think we need to kind of re. Engage with economists and policymakers rather than basically putting tech executives right on under the limelight and asking them or people who developed certain aspects of new AI technologies, what do they think society should look like? Right. What society should look like is a conversation that we all need to be involved in. It's a conversation that involves thinking kind of economically, strategically and from an incentive kind of basis. So I would say that again, there are many ways that you can run society. There are many different economic approaches that you can take. Right. Historically, the question about universal basic income is not just one related to technology. Right. It's one that you can choose. Right. In a welfare state. Right. To pursue under a variety of circumstances. Right. Universal basic income was not a necessary component during previous kind of large economic transformations and disruptions. However, that doesn't make it something that you discount and throw out as a potential policy instrument. A lot of it depends also on how you intend over the long term to promote innovation. Right. Again, economic growth is driven by transformation. And if you don't have that transformation and if you don't kind of create the innovation capital, then innovation or economic growth will stagnate, your fiscal entries will stagnate and you will end up in a regressive economy which doesn't really do very much. And that's not good for anybody because kind of our entire capitalist system in the US is built around the fact that we're encouraging growth and innovation. Right. So UBI may not be the best approach in the U.S. right. Because it may stifle innovation. And that's kind of a main engine, again, in the EU there is more of a footing towards, of growing that innovation capacity. Especially since Britain left the European Union, the EU has been trying to reposition itself as an engine for growth. And by the way, Ukraine long term plays into that as well. Because Ukraine has a huge technical kind of labor force, lower marginal labor costs, a lot of innovation capacity. And so all of these conversations kind of have a confluence right now. Again, it's not about, you know, universal basic income because we're going to eradicate human employment. We're nowhere near eradicating human employment. However, that doesn't mean that UBI is not something that we want to be discussing in broader terms in terms of how we build effective welfare states. Right. By the way, in the US UBI would be very difficult simply because of our entrenched healthcare system. Right. Simply because of how we run our education system kind of at state level. It's much more complicated than just saying we need to give money to a certain segment of the population every month. It's a kind of coherent economic strategy across the whole of society. And it should not be driven primarily by kind of fears or projections about technology change. It should be driven by kind of principled decision making about the kind of society that we want to build over the long term. Right. And we're nowhere near having those conversations yet, unfortunately.

Yeah. And James, this has been such an insightful conversation. You know, we've been able to touch on so many things from, you know, changes in the labor markets to how and when maybe government should be involved when it comes to, you know, artificial intelligence and, and creating social safety nets. But maybe as we wrap up, what is maybe your one important takeaway that you want people to hear today, specifically when it comes to how artificial intelligence is impacting and will continue to impact society.

So for me, the biggest topic today is economic and community resilience. And that's why we talk about kind of community resilience being at the core of technology adoption and as a result, building the capacity of our local communities to use technology effectively. To be knowledgeable about technology and to choose how that technology gets integrated into their everyday lives is crucially important. So from that perspective, strengthening, you know, tech based education. Right. Strengthening the public's ability to understand the changes that are taking place and also, again, building that capacity at the very local level to start businesses and innovate. Not to wait on big tech to do the innovation for us, but to have innovation really take place at the grassroots level. I think that's what's going to actually enable our economy to weather any set of transformations. Right. However fundamental they may seem when we discuss them now. But it's really bringing it down to that hyperlocal context and allowing communities to have that voice and those capabilities and those resources which is going to keep us able to transform and innovate and build. Right? Which is, by the way, what, you know, has made America so strong in, in the past.

That's so much, so much good content here. I, I can't wait for everyone to be able to, to, you know, hear this conversation and to even read about it more. So James Hodson, CEO of AI for Good Foundation. James, thank you so much for joining the Everyday AI Show. We really appreciate it.

Thank you, Jordan.

And hey, as a reminder, there's a lot of different initiatives that James mentioned, some different pieces of the work that they're doing throughout the world. So make sure if you haven't already, to subscribe to our daily newsletter@your everydayai.com we're going to be recapping this conversation and even getting to things that we didn't have the time to touch on. So make sure you check that out in the newsletter. So thank you for joining us and we have hope to see you back for more Everyday AI.

Thanks y'. All.

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