The following are the outputs of the captioning taken during an IGF intervention. Although it is largely accurate, in some cases it may be incomplete or inaccurate due to inaudible passages or transcription errors. It is posted as an aid, but should not be treated as an authoritative record.
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>> JUDITH VEGA: Hi, good morning, everyone. Thank you so much for joining us. I'm going to give us a couple of more minutes to get settled. I'm going to invite everyone to come up and take a seat up here on this round table just so we're all a bit closer.
[PAUSE]
>> JUDITH VEGA: Okay, great, I think we can go ahead and get started. Good Microsoft Corporation everyone and thank you so much for joining us here at the 2025 Internet Governance Forum in Oslo and a very warm welcome to everyone joining in via the live stream. My name is Judith Vega and I'm a specialist at the World Economic Forum networking on governance and policy for technologies.
It is my sincere pleasure to be your moderator for today on this session focusing on the future of digital public infrastructure and Artificial Intelligence.
As we all get settled, I want to start today by making a bold claim. I'm going to ask us a question here. Most of us on a daily basis interact with DPI protocols and tools, and I'll get to prove my point in a second.
By show of hands, can I ask how many of you here in this room have a smartphone with face ID?
All right. That's not bad for people at the IGF. That's pretty good. All right.
How many of have you social media accounts that require log‑in information and a password? It doesn't have to be Facebook. It can be Linked In, whatever your choice S. all right. That's most of us again. And how many of you use digital payment systems?
There's a reason most of us raised our hands. Over the past decade DPI has been the cornerstone that allows us to navigate and participate in society through its core components which are digital identity digital payment systems and data exchange. There's a wide variety of ranges we can do that in and most of the innovations in those three sections have come from the private sector in last couple of years. The question we asked today is not does DPI work or how does it work but rather how do we get it to work well, in the future, in a way that is globally scaled, interoperable and secure? We pose perhaps the answer lies in AI, open source AI very specifically and if so then what are the roles of the public and private sector. What can they both play to make sure it comes to fruition? I'm thrilled to be joined by three outstanding panelists.
To my right I have Larry Wade, global ahead of compliance for blockchain crypto and currencies offering a critical perspective on financial innovation and regulatory frameworks. Thank you for being here, Larry, and to his right Melinda policy private director at meta who brings a wealth of experience in platform governance and then joining remotely we have Judith Okonlow, a pioneer in diverse technologies and open innovation especially across the African continent.
I remind you this is open forum invite your reflections whether here in person or joining us online your voice essential to this dialogue.
With that I begin with a question for Melinda. Melinda, Meta has broken ground with their open AI source mod. Can I ask you how does meta view AI? What does it feel the future of AI is? What is this AI integration across region and jurisdictionally and how do you see open source AI contributing to the development of DPI protocols globally?
>> MELINDA: I think that was three questions or so. Really happy to be here. Thanks for organizing.
Yes, so just to level set for a minute about AI and meta. We are both a developer and provider of large language model that we call LAMA. Produced multiple versions at this point and also build services on top of our large language model. Just for a minute our open source approach to really means that we make a very powerful large language model available for free to anyone to build on it. This is an incredible advantage to anyone who wants to have access to cutting edge technology and it allows a really impressive level of customization for developers who want to provide solutions for their companies, constituents, stakeholders, countries and region. We think open source is an incredibly powerful tool to accelerate the adoption and use and implementation of AI but most importantly to make it as useful as possible for as cheap as possible to people.
We also are very focused on building and incorporating AI into our existing services and developing new services based on AI. If you're a user of our apps you will have seen that we've already added generative AI features into our apps that let you but also to ask questions and get answers. We've also recently launched a standalone app that you can have ongoing conversations with, that you can talk to, ask for recommendations, that kind of thing. And so we've really see the future of AI as a personalized experience, a personalized assistant for you in your day‑to‑day life, and I think we are getting increasingly closer to that being a reality. Recently for those of you who may have seen our booth that ‑‑ our meta booth has our glasses, our meta AI assistant has been integrated with Rayban Meta eyeglasses. That means you can wear these glasses and walk around and talk to the glasses and ask the glasses, hey, I'm in Oslo, what am I looking at? Or what does this sign say in Norwegian? Can you translate it for me? So these are just really concrete, easy fun examples of the way that AI and AI powered by open source technology is really coming into our daily lives and provide ago lot of value.
>> JUDITH VEGA: Thank you, Melinda. I have a follow‑up. You said that this is providing daily value which I think is very, very true. In AI providing value to these technologies and these new products, where do you see these being integrated the most? Where do you find that people are using this sort of open AI source the most?
>> MELINDA: So I mean when you think about our open source models that are ‑‑ have been downloaded millions and millions of times, I mean, we're track ago lot of uses in really groundbreaking ways. So our LAMA models are being deployed to make scientific discoveries for advances in health research. They're being deployed in small communities around the world to help kids with their homework in a local language. You know, they're just being deployed in really creative interesting ways that are helping people day‑to‑day. I think we tend to think what's the cutest feature on this app, and that's fun but I think we shouldn't lose sight of the importance of this foundational technology and the value that these models can provide and so we provide ‑‑ we've run programs where we provide impact grants to, you know, entities that have interesting pitches and ideas and we provide technical assistance. The sky is really the limit in terms of how AI can be leveraged to solve local problems in a really inexpensive way.
>> JUDITH VEGA: Thank you. I want to stay with this topic of value, and Larry, I want to turn to you. PayPal is a leader in digital payment systems. How does it see its value or the transfer of financial values in sort of this next era of the Internet with AI? How does it see it changing with AI.
>> LARRY COMPLIANCE: It's interesting. It's pretty much an optimization layer in a way. So our CEO Alice Chris likes to say our goal is to revolutionize commerce. We have this two sided networks. We get to see consumers and merchants, 400 million wallets in 200 countries. Robust ecosystem that we get to see.
When you look at from my vantage point distributive technology, blockchain, digital assets, AI is going to be essential in a few ways, right? Think about just onboarding customers. Believe it or not, depending on where you are in the world, is extremely challenging. So the customer identification process, know your customer, know your business, especially on a small business side it can be challenging. Being able to utilize tools to just AI to say all right there's additional attributes we can look at in order to gain comfort with onboarding customer segment which now we can facilitate providing different services we couldn't in the past. Sounds simple but when you're talking about compliance or risk management globally that's essential.
Blocking and tackling on fraud and financial crimes just making sure that people ‑‑ when you're dealing with money, that ‑‑ I like to say the Internet kind of 2.0, democratized information. Beautiful. This Web three is democratizing value and when you're democratizing value the stakes are even higher because everyone needs to transact. So being able to enable a safer environment to enhance and improve the velocity of transactions, that's going to be essential there. So, again, fraud, BSA, AML sanctions, et cetera. And then also just the only experience. What are people doing and how? AI allows us to see patterns that we typically are unable to see. So we launched the first stable coin by a major financial institution that was regulated, PYUSD. And stable coin, again, many benefits to it. Deploying AI right now allows us to kind of start seeing okay where is it being used? How is it being used? What potential use cases? How can we allow this tool but also faster cheaper programmable value transfer witness to financialality? I'll say lastly tying the blockchain AI in as well is this notion of asset provenance, right? So we'll talk to merchants and let's just say you're Nike and you have the physical good and you have the digital representation as well, rights? When you start getting into physical and digital knowing what is valid is going to be extremely important. Well, think about when you have your digital twin, when you have AI generated outputs, being able to utilize kind of NFT technology to be able to put that stamp to say this is the real one, digital 101. That's also something that's going to be important so the digital identity component and also that's problem as well as optimizing for value transfer, that's kind of where AI is being integrated now. It's actually early days. I do think we're going to hit a point on a curve where we'll see exponential change.
>> JUDITH VEGA: On that exponential change you talk about, looking into the future, do you think that AI is going to be foundational for seamless integration between public digital wallets and then private digital wallets and services.
>> LARRY COMPLIANCE: Hundred percent. When you're dealing with value transfer, trust, compliance, it's essential. And you can't get it wrong. So when you can improve those kind of core tenants of how we're going to integrate with these wallets which are going to hold value, not only just, you know, FIAT value or advertise to the banking system, it will be also just bear assets that hold value that you want to just keep yourself. I think it will be essential and what they'll do is those experiences from the onboarding to the continuous monitoring, understanding what are the preventive and protective controls around this ecosystem, it will enhance that. Again it will also allow us to improve pattern recognition. Just being able to lower the likelihood of bad things happening, improve the experience to make sure that, you know, activities feel more seamless, that's what AI is good for. So I can't see it not being critical.
>> JUDITH VEGA: Thank you, Larry. I want to turn to Judith now if we can get her on the screen.
[PAUSE]
>> JUDITH VEGA: Hi, Judith, can you hear us?
>> JUDITH: Yes, I can. Can you hear me okay?
>> JUDITH VEGA: Yes, perfectly. Thank you so much for joining us.
I want to bring you in on this. We're talking about AI enhancing across DPI protocols and services. And you've done a lot of work on adoption of AI and integration and I want to ask you: Is there any particular area that you feel AI integration is particularly important? Where is it critical and are there any barriers to integrating open source AI across different regions and across different jurisdictions?
>> JUDITH OKONKWO: Yes. Thank you very much for the question. So I'll probably start with sort of like AI integration from an open source perspective and what it enables. For context the work that we do at MSU3D over sort of like the last decade has been ecosystem developmental the emotive technology so augmented virtual and mixed reality across Africa and as you can imagine there have been lots of barriers, right? From the perspective of access, infrastructure, all of that, for people to engage with these technologies, but even more importantly to be able to build with them for society. And I think one of the exciting things about integration of open source AI is that it allows us to tart to benefit from the convergence of these technologies. Really I think it isn't either/or when it comes to these technologies. We can see them coming together to really create products and services that can have a real benefit for society. When I think about what AI is making possible, especially open source AI, it's, you know, driving experimentation. It's allowing people to build but not have to start from scratch which is really, really important and to give you some context about what that looks like in practice for things happening at the emotive technology end of things with our work in some of our communities we have worked on a number of projects which I'll mention which are now really benefiting from the availability of open source AI tools. One project is a product called autism VR, which was designed as voice driven virtual reality game and the idea with this was creating something that would really start to kind of like shift the needle when it comes to the lack of awareness about neuro diversity, particularly among children because of course we're coming from a context where mental health is severely under resourced and where the lack of information about neuro diversity has really caused a lot of not just discrimination but exclusion for children. That should not happen. With the advances we have the availability of open source AI tools it's now possible to say not only are we going to integrate that voice driven component which makes it a much more engaging tool for the general population to engage with but we can also leverage language capability, right? Because we're coming from communities where several different languages are spoken on a daily basis and building solely for English for example has really limited the public ability to engage. Another example I wanted to cite related to that is VR schools initiative that we have and this is now looking at deploying this technology and really resource constrained learning environments. How can we go into a situation where, for example, off cool without, you know, infrastructure for things like science experiments, right? And that kind of resource constrained environment can you bridge the gap with emotive tools. Can you create a VR lab where students have been able to do simulations and now that's one step there but then imagine the ability to have agents who can act as guide on top of these open source AI tools that can then provide the support within the emotive environments for those students' learning. What that does is then make this a tool that you can then deploy not just in a classroom setting, you know, but also in much more informal context. I think when we think about situations where you have, you know, children who unfortunately are out of school you then get to the concept of almost taking the school to the child where they are if you're able to have this combination but you know moving from that to your question about you know the barriers integration of open source, I think we see much of the same constraints we've seen from the emotive technology side. I think a major one to talk about is skills, the sort of like capacity gap and what needs to be done about that. I think to be able to leverage open source AI we need to invest significantly in educating people and making sure that we have the knowledge and skills locally to build and doing this across the board, right? And I think alongside that there's definitely even just sort of like a link to the awareness piece, right, a lot of work that needs to be done from a digital literacy perspective.
Other barriers to this integration, I would definitely talk about infrastructure. Much of the same sort of ‑‑ also existing with open source AI, particularly when it comes to the Internet. And I think in a country like Nigeria for example it's really great to see the investment that's happening now in that space to make sick changes and get as many people online as possible.
And then data, you know. We need localized data sets. We need to be able to train models so that they're relevant for us and I know that that's work that's currently ongoing. Lots of fantastic initiatives. Masacani is one. There are barriers but the work has been done although there is a lot more to be done.
>> JUDITH VEGA: Thank you so much, Judith. I love when panelists have my first name. It's lovely to call on you.
I love this idea of AI being useful to build upon and get us ready for sort of this next phase of tools really being deployed and used for public good and good consumption. I want to turn to Melinda now.
I wonder, you know, part of critical to sort of DPI building this idea of hardware and Meta has begun to produce good hardware, valuable hardware. What do you think is important to be able to scale that hardware? What is a role of AI there?
>> MELINDA: So I think we want to make products useful to people. Part of this is we've launched the glasses a few years ago and continue to roll them out to more countries, as from the AI perspective as it gets better and is more useful. Part of it is iterative process. Understanding these are new concepts. These are wearing AI on your face is new. I think what we have to do is test things out and see how do people use them? What are the use cases? How do people find them useful? And then we bake that back in to the development process for our products. And so I think it's a learning process over time. Obviously there are constraints in terms of how to actually build something that fits your face and has a battery that works and can ‑‑ you know, there's questions around processing and all of that. But I think the biggest challenges are really around adoption and how are people planning to use these and making them available in as many countries as possible, making the AI as useful to as many places as possible, and so part of that Judith the prior panelist was talking about making local data available and I think that is crucial to unlocking the power of AI. We train our AI on a wide variety of data but we don't have access to a lot of data that would make the models most useful to local communities and theres again why the open source component is so important because local developers can build on top of our model by adding datasets that are relevant for that country, community, region.
And so I think all of this has to kind of work together to figure out, you know, what is most useful in terms of having AI available to you? Is it in your app? Is it on your face? Is it all of it? And I think it's exciting. We'll see a lot of different approaches from different companies in how to make AI products as relevant as useful as possible for people's day‑to‑day lives.
>> JUDITH VEGA: Thank you very much. I'm actually going to open this question up to any of our panelists. We recognize the importance of localized data, datasets and integration and harmonization of these technologies. I want to ask: This is from the private sector. We know there's development here. What would help, what would be beneficial from the public sector to be able to achieve these goals?
>> LARRY COMPLIANCE: I can take that one. Something Judith said and you hit it as well. Going ‑‑ she said bring it to where the kids are. There's a reason why there are so many underbanked people in the world. A lot of it has to do with just the overall risk tolerance of institutions that are serving them, whether it's their own policies or again restrictions placed on them from whatever kind of local regime from a regulatory perspective. So just wanted to hit on that same thing. Once you kind of can use AI to solve that more localization additional attributes, hey, here's additional data that can actually derisk this customer, again, opens up things. But to answer your question and this is something I have to deal with all the time. It's being able to bring the regulators and government the along the journey with you. It has to be a public‑private partnership. Again, when you're dealing with these very complex topics that impact society in such systemic ways, you have to make sure that those who are making the policies are not making them in silos. That you're knowledge sharing and hopefully that governing body, wherever they are, they are kind of giving you the ability to experiment. So there's this constant push and pull of there's rule making, here's why that sounds great but it's not feasible. We do need rules because we need to be able to ensure we all have a kind of general set of parameters to play with. So I think that back and forth relationship the kind of minimum expectations guiding principles Mitchell mal requirements and being comfortable with when information changes both sides being able to kind of change with it I think is important for all of this to flourish.
>> JUDITH VEGA: Thank you, Larry. On your point I want to open this up now to the rest of the room and also I'm joined by Augustina Callegari at the World Economic Forum who's serving as our online moderator. Please, if you have any questions for our panel, either online or in person, this is your time, please raise your hand and join the conversation.
[PAUSE]
>> JUDITH VEGA: Do we have anyone online?
[PAUSE]
>> AUGUSTINA CALLEGARI: I've question here online. The question if there are any examples of cooperation for open source AI sharing?
[PAUSE]
>> AUGUSTINA CALLEGARI: That's the question for you.
>> JUDITH: Hello. Thank you very much for the question. So any examples of south to south open source sharing. Sort of like the examples that I'm most familiar with at the moment are around community. So one of the things that has really driven the concept of open source on the continent that I know about is the open source community, the African version and they have collaborated across board with communities and in other south countries and I want to highlight this because the concept of open source has given people pause several times on the continent because it's the idea of you know there's this sort of like reaction what you want me to make it freely available, then how are we going to make money, how are we going to benefit economically that sort of thing. There's been a real need for education around open source and kind of all the afford dances that then provides for everyone the people building in the first instance. That's what I would mention but I'm not aware of many other like sort of core examples and I'll definitely look that up.
Thank you.
>> Augustina Callegari: There is another question that ‑‑ related to what Larry was saying about working with policymakers. So how do you ensure continuous sharing of knowledge with policymakers?
>> LARRY COMPLIANCE: So I think, one, it's having a respectful honest relationship with the regulators you are working with for your particular business and ensuring you're having engagement with the actual kind of government officials. Again, not only in your jurisdiction but in those jurisdictions that you are attaching with. So a couple things. Take the digital asset business in PayPal. Two sided network. How do you integrate this technology into rails to enable faster, cheaper more programmable just value transfer within that ecosystem. One, I want to ‑‑ what Judith mentioned on open source the reason why we chose to use open source protocols was, one, how do you attract the best talent to work on protocols? Needs to be open source. Two, how do you not pick winners and losers? Open source. So like just for anyone who's kind of asking that as well, we really thought about that too even with PYUSD. They were on a open source blockchains. There's obviously a need for private at times but again if we're going to allow these technologies to grow, open source tends to be the best approach.
But again, just making sure that you have those regular cadences, it sounds really simple, but it's challenging. Who are those regulators? Who are those policymakers? What are the regular cadences? How are we bringing value to them? How are we kind of self reporting when things are going right or wrong before they ask. A lot of this is about trust. There are brilliant people working on these things, right? Engineering is not really the issue right now if you think about it. Take all these amazing technologies we have right now. Whether it's AI or quantum computing or block change digital assets. They're brilliant minds working on them. The real gaps are around all the people who are going to help facilitate the introduction of these technologies into society, and that's on the policy side and, again, that's in the businesses. So having just that respectful honest transparent relationship in knowledge sharing on a frequent basis goes a long way.
>> JUDITH VEGA: I'm going to interrupt the QA. I want to follow up on this. Trust is earned, right? Something that requires a sustained period of interaction. Does PayPal find that it becomes more trustworthy in using open source?
>> LARRY COMPLIANCE: I would say I didn't, and it's interesting because it's not only just trust. So take a step back. We have a stable coin with our name on T. we work with other institutions. Being able to say, hey, yes this is PayPal stable coin but it's on open source blockchain allows that institution to feel like they have more skin in the game when we're working with regulators. And unfortunately I get to speak to regulators all around the world. I was in Singapore a couple weeks ago and then in the UK meeting FDA and dealing with the department of financial services literally every other week and all the different alphabet super, these open source protocols also allows them to have a little bit more agency on how they evaluate. I found it to be beneficial. With that said, I do think there is the need for some walled gardens and that's where this whole notion of interoperability is going to come into play because there are times when you need an extra net or a closed, you know, ecosystem, but then how do you ensure there's interoperability protocol to interact outside when the time is needed? I think that's also part of that open source story and how Ulandi Exner see both of those playing out.
>> JUDITH VEGA: Can I ask when those times are when you need a closed garden or intranet?
>> LARRY COMPLIANCE: Sure. Let's say you're a big bank and you just did a syndicated wind farm deal in Canada and the arranging bank now via some smart contract it's determined whatever threshold is met now we can disburse out payments. Does everyone need to see that? No. Duds everyone need to see how the Visa MasterCard how participants ‑‑ no, right? Do you want to see ‑‑ would you want people to see all of your PayPal transactions? No. So I think it's fine to have a little bit of privacy. I think privacy can actually be important and it's funny because we're talking open source but then now we're going to privacy and this is why this is all so complicated but also why it's so nun because we are solving new problems that ‑‑ I don't think anybody's had to think about on this scale because these technologies are so revolutionary. Definitely times where it needs to be between us but the ultimately, you know, both are needed.
>> JUDITH VEGA: I couldn't agree more and I certainly don't want everyone seeing my transactions but with that I open it up again to the floor once more.
Yes, there's a question in the back.
>> AUDIENCE MEMBER: Hi, can you hear me? My name is Marin, researcher for ID for change, NGO that works at the intersection of digital technology and social justice. So my question, it's a two part question. One is a more basic question. I want to understand better what you think or how do you see ‑‑ what do you ‑‑ how do you define an open source AI? Because the issue is one concern we have is even when we talk about open source and possibilities of innovation that it allows for it, it seems that the foundational models are still being controlled by few actors. It's not really democratized. So what is ‑‑ for me open source AI is something also equivalent to a democratized access and development of AI. If the core foundational model still controlled by a few actors, what is the ‑‑ how do you define open source AI? And secondly I think you mentioned in one of your interventions that open source when you integrate open source AI into DPI it also allows agency to regulators to validate. I want to understand what are the benefits of open source when it is integrated with DPA? What are the ‑‑ how does it allow the public actors to evaluate? Because when DPAs essentially used for various core governance aspects and it can hinge on the rights of the citizens. So how does it ‑‑ how does open source allow in the regulators to have more oversight over the DPA applications that are being used for governance structures?
>> LARRY COMPLIANCE: I'll give you kind of my thought. That's a great question, by the way. Thank you.
So let's kind of go back to ‑‑ I'll use this Internet 2.03.0 example again. So in Internet 2.0, you had these brilliant engineers that created this infrastructure. Who extracted value from that infrastructure layer? None of the infrastructure builders. All the value was at the application layer pretty much. I also think that's why we have some of the issues we have now, right? But again, there was tons of innovation. We're moving forward.
The way I think about open source is that infrastructure layer is open where developers can work and build and there will be times that they build applications that are open source themselves, and then there will be times where applications do need to be a little bit closed but ultimately if you don't have the open source infrastructure layer now you also have that problem at the application layer again and being able to have value transfer mechanisms align to the infrastructure layer is a really important idea because it incentivizes brilliant minds to work on them because they have upside and also it allows for a little bit more just competition on what's going to win. Because ultimately if I can extract value from various infrastructure layers, what's going to make me pick one over the other? Maybe it's just better. That's kind of how I think about that.
And then your question on the regulator side again, you're dealing with people who you have a lot more expertise than they do because they have such wide scopes and you're living it every day. So if you have a starting point where there's an understanding of what the kind of infrastructure is, as you're building more complex products on top of it, the discussions are a little easier. So I mean it happens all the time just with what I have to do just again in the digital asset and distributer technology space, right? If I come in and say, hey, we want to build this new product that, you know, allows for X but we're building it on this open source blockchain that you are familiar with at a minimum there's a little bit of comfort in what we're trying to pitch. Now the complication is on that actual innovation on top of that. I don't know if that helps a little bit but it's kind of like this beautiful dance in a way.
>> Thank you, Larry. I know we have a question here but this ‑‑ if you like to go ahead.
>> Thank you. My name is Helojen. There's a fundamental conflict in the payment systems in that payment systems have to be accurate to a sense and they have to be auditable and followed. While the AI is typical in detecting interesting patterns coming up and surprising answers and being absolutely hopeless at explaining how they achieve them, can you talk about a little bit about how you mitigate that conflict when you embed AI in payment systems.
>> LARRY COMPLIANCE: Excellent question. This is fun. The way I like to think about things is ‑‑ let's take payments here. 80 percent of what we need to do, we can leverage best practices in just tried and true, hey, we know this works. So overlaying AI again it's about optimization. We wouldn't throw out all of the policies, procedures, and controls that are already developed to make sure that we can adhere. Even though, by the way, there is a lot of friction and a lot of errors even in the existing system, right? A lot of true‑ups and things of that nature. The way I see AI integrating to payments is not saying we're just going to rely on AI for this. We have been doing XYZ. PayPal we have been moving money for 20 years. Doing it well. Overlaying AI now can give a better customer experience could actually now find those tail situations and can better refine us making sure we meet any obligations to customers regulators, et cetera. Again, optimization rather than pure alliance. That's how I see that. It's a partnership. It's a good question.
>> JUDITH VEGA: Thank you Larry. We have a gentleman here to the right.
>> Thank you. My name is Satish and I have a long background in open source. I am prejudice part of ICANN and dot Asia organization. I sense a little bit of uncertainty when you referred to open source AI, because open source from the last 20 plus years of working with open source usually means code. Does means stuff that you write in C or java or whatever. These days code is open source model is kind of free. Most organizations including Microsoft at least code and open source ‑‑ when we started out 25 years ago it is very extraordinary. Today it isn't. The second part is open source model waits. That is new to AI. You train it and come out with waits that is what the model decides is going to respond to questions. Open sourcing that is not a very with the articulated concept. The third thing is open source datasets. I'd like to know what precisely you mean when you refer to open source AI.
Thank you.
>> JUDITH VEGA: Thank you so much for the question and I'll give a little bit of background. So when we ‑‑ we've had these extensive conversations at the forum now in the work that we do about what open source means particularly as it pertains to digital infrastructure. Normally when you talk about DPI, the P in public means different things to different people. What we've landed on that DPI does not necessarily mean public as in public sector but rather public as in common or generally available to the public. And that's also the definition that we leverage. We're the common consensus that we leverage for open source. Not that it is, again, public sector and public driven but rather that it's ubiquitous and can be commonly found. That it's something that can be found and used leveraged across various districts, region regardless of its source and then can be built upon by different actors across different sectors. So really grounding ourselves in that open source whether it be trading model or just the code itself but it is common, open, free and can be accessible is what we mean when we refer to sort of open source and I think it's what Melinda was referring to. These are just protocols available, right? Anyone can download them if you have the right hardware you can download them, train them, build upon them deploy them and integrate them to different models or different technologies. That's what we mean ‑‑ the P for us is common, not publicly available not necessarily publicly driven.
>> LARRY COMPLIANCE: Just to add to that the notion is going to be important. I keep going back to regulation and minimum requirements and if you can kind of start with the same ingredients for lack of a better words how your cake comes out is going to be dependent upon how you mix those ingredients, manipulate and how you bake it. Ultimately we kind of start with the same ingredients. If you start with the same ingredients it allows us to have ‑‑ allows those governing to have a better starting point to have sensible reasonable regulations and requirements. So again that's why lean towards if each model was bespoke and those governments and regulators had to start with something net neut every single time that would be quite challenging but if we all kind of have minimal requirements where there are certain kind of like known protocols that have been adopted to start with, I think it will be easier to manage some of this. It's going to be quite challenging honestly because here's something I run into. What one regularity in one part of the world what matters to that group can be dramatically difference. I mentioned MSC and all these other names. It's very clear when you're doing business in the UK consumer protection is front and center. You have financial promotions and consumer duty and things like that. Yes, it matters in the U.S. but not as much as it matters in the UK when I'm ‑‑
>> JUDITH VEGA: Are you saying that we're unprotected in the US.
>> LARRY COMPLIANCE: Not saying that. Just getting to kind of see each kind of government and regime has their own thing. For example, you look at the EU and MECA. It's great. They put out digital asset regulation but if you kind of back door it they're saying but we really want EU denominator stable coin. You're dealing with regulators trying to learn and depending on what their priorities are they're trying to force those as well. It's going to be complicated no matter what you do the more we can kind of have a common nomenclature and starting point to at least negotiate with I think is going to make things easier. It's going to be challenging. This is global adoption for all of these technologies.
>> JUDITH VEGA: Thank you. I know we had a question online Augustina Callegari I'll turn it to you.
>> AUGUSTINA CALLEGARI: How do we see private technology companies play a role in API and AI for social landscape. Within DPI digital payment have been solved.
>> LARRY COMPLIANCE: What was the last part? Unsolved?
>> AUGUSTINA CALLEGARI: Unsolved within DPI now that's the way it's framed. Now the fundamental topics like digital ID payments have been solved basically, yeah, it's asking about if related to digital ID and payment have been solved.
>> LARRY COMPLIANCE: Have they been solved?
>> JUDITH VEGA: I'm going to let Judith come in for a minute. Judith, you're still with us?
>> JUDITH: Yes, I am.
So sort of like to jump in that last bit about the digital ID and payments haven't been solved. If that's the question? Then I think yeah definitely should still ab question because I would say not solved in very many part of the world but to the first part which is around how private technology companies can come in and I want to talk about AI for social good. I'm not sure if Melinda is still with us but one of the initiatives Meta for example is driving on the continent linked to its large language model LAMA impact accelerators and the initiatives where they are incentivizing communities, developers to develop on top of the large language models and create projects that will, you know, in some positive way impact society. The fund has been going on for a couple of years, I believe, but the current iteration and applications are still open for that, what we are seeing is a handshake with governments. For example in Nigeria it's in partnership with the ministry that okay have you feel is a digital economy. I think what's interesting about that is we're starting to see the multistakeholder approach to driving AI for social good, right? I know when we talk about DPIs we talk a lot about public‑private partnerships and the role that they have in accelerating things and I think we start to see that with initiatives like this. And there are a number of others. I mean, in country in Nigeria which I'll reference for my examples alongside things like MPAC, other initiatives from say the Gates Foundation where they're currently investing with the government to create an AI scaling hub that will then allow more people and country to be able to do a number of things, build on models, work on datasets, all of the things that will advance a national AI strategy. So this ‑‑ yeah, a huge role for private technology companies. Alongside that for all of the people that will engage with them. And I think basically from the regulatory side of things, so governments there is a real need to determine what that engagement looks like and how it will impact people, how it will impact citizens and of course there's the citizen piece where people then have to have a voice in saying how these things will affect them and I think when we start to talk about that in that voice we have to think about the digital literacy that's required to enable that.
>> LARRY COMPLIANCE: I totally agree you with digital identity and payment has not been solved. Yes, there have been, you know, enhancements and we're moving towards but no it's actually a great opportunity for people to try to tackle and I'll just one thing to add what Judith said I think so it's irresponsible for private companies to create these world changing technologies and not lean into educating those that have to regulate them. And again that's just my own personal. Thankfully I get to from PayPal kind of lean in on the regulated side. That's how me and my team kind of go about it but just say hey we're going to create something that is complex, disruptive, beneficial, here you go, you figure it out on your own I think that will just cause more confusion, angst for everyone involved. I think it's building, leaning in, but then also educating, communicating and understanding that even though frustrating you need to bring governments and regulators along for the journey because that's the society portion of this whole thing.
>> JUDITH VEGA: Thank you so much. I want to give a final opportunity to any other guests.
Oh, yes, please go ahead, sir.
>> hello. Nick from Norwegian tax administration. I'm represent ago large public sector agency in Norway. We're using a lot of machine basic learning general AI tools as copilots for productivity but we are rather reserved as using advanced AI like deep learning based AI and generative AI as well for decision making that affect the citizens. Because we basically can't really explain the results at a satisfactory level. I wonder to what degree do you view open source as helping us realizing explainable AI? I mean open weights or open source code can provide trust on the formal level but in my view it does little in the way of actually explaining the results and decisions on a level that's understandable to the citizen.
>> LARRY COMPLIANCE: Thank you for that. I would say this is where we all take a step back and be humble that these are very interesting and challenging questions. Having that lower level, hey, we're kind of playing and experimenting in this I think that would just be great. And then also leaning into those partners who you do work with on the tech side and being able to share your results and see if they can help as well but I think that it's going to be important to make sure that government agencies are kind of in a way mirroring the private sector. It's not that bifurcation will be so great in the long run that we could end up having problems down the road. To your point you have responsibility to the citizens to make sure we get this right and that's what we're doing now but then also experimenting here to make sure you're keeping up with the technology. That way when it's ready for prime time which a lot of this is not yet you can kind of do the cult over. That's kind of how I see that. Judith, I don't know if you have ‑‑ Judith and Judith I don't know if you guys have Internet Governance in.
>> JUDITH GAINS: I'll let panelist Judith come in if she can and then we only have about a minute left. I'll go ahead and wrap up.
No? Okay. I will go ahead and offer some thoughts on this question and also give us some reflections. We talk a lot about trust at the forum and I'm very happy to be joined by Daniel who's the head of governance at the World Economic Forum and we talk a lot about trustworthy decision making. That's strategically important to decision maker have sort of an obligation to the public and you're right, when we talk about AI we have the luxury with spending all of our days in details and models and how to play with them in private public cooperation but there's a large number of people or groups of people aggregated throughout the world that don't have the luxury to do that every day. To your point it then becomes necessary to be able to explain and communicate these things in simplified terms so the user is not only protected but protected through being informed and well‑informed so the user can take action and steps and sort of better decision making themselves or demonstrate their preferences somehow. That takes again cooperations with private and public, these efforts need to be driven jointly. And I sort of want to wrap up by inviting all of us to think about the future. AI isn't this abstract thing anymore that's being talked about every so often on large new outlets. It's being deployed every single day and used by both public and private sectors to improve and enhance DPI and the technologies that we all use whether we're sending money across PayPal or Zelle or someone abroad or different country to our friends after dinner it's something that we're using to access civic participation, public life, in some countries even voting and other forms of essentials of participation. It's the way we express our citizenry and autonomy. As we sort of venture into this future together I invite all of us to think about what kind of future is it that we want and we're not passive users of this technology. We can think about these things, make decisions every day and especially the people in this room we all continue to be well‑informed and advocate for the sort of technologies that we wanted being deployed in the bedrock of our every day lives. So thank you again. I thank our lovely panelists, our online moderator and thank you so much for joining us. If you have any questions we're here for the next ten minutes, please stick around. Thank you again. Have a lovely day.
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