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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>> VALERIA BETANCOURT: Welcome, everybody. Thank you so much for your presence here. This session is going to look at the case for local artificial intelligence innovation pathways to harness AI for benefit of humanity.
We have -- I have the privilege to moderate this panel today. As the Global Digital Compact and the scores, there is an imperative for digital cooperation to harness the power of artificial intelligence innovation for the benefit of humanity.
Evidence of [?] in several parts of the world, particularly in the context of the Global South, increasingly points to the importance of contextually grounded artificial intelligence, innovation for a just and sustainable digital transition.
This session is going to look at key dimensions of local artificial intelligence, inclusivity, [?], and intentionality. Our speakers from the expertise and on viewpoints will help us to get a deeper understanding of how these dimensions played out for local AI that is contextual and that contributes to the well-being of people and planet.
So I have the pleasure of having Anita Gurumurthy for IT for Change to just help us frame the conversation that we will have. And I will invite Anita in to come and please frame the conversation, set the ground floor and the tone for the conversation with our panelists.
>> ANITA GURUMURTHY: Thank you, Valeria Betancourt. Honour to be part of this panel.
I think at the starting point when we look at just and sustainable transition is to reconcile two things. On the one hand, you have an unequal distribution of AI capabilities. And on the other you actually have, you know, an increasing set of demands going to climate and energy.
And the impact of innovation on a planetary scale. Therefore the question is how do we democratize innovation and look at ideas of scale afresh, because the models we have today are on planetary scale.
Both the production and consumption of AI innovation need to be cognizant of planetary boundaries. Essentially then what is this idea of local AI? Is it different from ideas of localizing AI? Is there a concept such as local AI? Will that even work?
I just want to place before you some statistics and we have a colleague online who will speak about this from UN Trade and Development from the digital economy report that was brought out by the UN.
And I want to quote some statistics.
Between 2022 and 2025, AI-related investment doubled from 100 to $200 billion. This is about three times the global spending on climate change adaption. So we're investing much more on R&D for AI and much less on what we need to do to, in many ways, look at the energy question and the water question.
Supercomputing chips have enabled some energy efficiency. But market trends suggest this will not make way for developing models differently. It's going to support bigger, more complex Large Language Models in turn mitigating the marginal energy savings because chips are becoming more efficient.
The efficiencies in compute are really not necessarily going to translate into some type of respite for the kind of climate change impacts.
Now, I want to give you, you know, this is just for shock value, the computational demand to fuel AI growth is estimated to double every hundred days. As we speak. Due to increased energy demand of data centers. And this is a very, very vital concern.
We also know that around the world there have been water disputes, you know, because of this. So there is this big conundrum, we do need and we do want small is beautiful models. But are they plausible? Are they probable?
And while there is the strong case for diversified local models, I want to really underscore that there are lots of people already working on this. There are have some people, governments that are investing in this and there are communities that are investing in this.
And these are very important because from an Anglo-centric perspective, we think everything's working well enough. LLMs are doing great for us, ChatGPT is very useful and certainly so to some extent. But what we ignore is there is a western cultural homogenization and these AI platforms amplify systemic injustices.
We are doing more than excluding non-English speakers. We're changing the way in which we look at the world. Erasing cultural histories and ways of thinking. We need to retain the structures of our multilingual societies. So those structures allow us to think differently and decolonize scientific advancement and innovation in AI.
So how do we build our own computational grammar? This is the question I think that's really important. And we reject the unified global system, but the question is, are these smaller autonomous systems even possible? Can we do this for minoritized communities, minoritized languages?
And the second question is, many of the efforts in this fragmented set of communities are really not able to come together and perhaps there is a way to bring them in dialogue and enable them to collaborate.
So this tension between pluralism, that is all necessary, and generalized models that seem to be the way, the only way AI models are developing in the market, is this tension is where the sweet spot of investigation actually lies.
And with that, I give it back.
>> VALERIA BETANCOURT: Thank you, Anita. Thank you for illustrating also why enabling [?] accountability is a must in the way in which artificial intelligence is conceptualized, designed, and deployed.
Without -- let's go to the first round of conversation, I mentioned that we'll be digging into three dimensions of local AI inclusivity, ingenuity and intentionality.
And the first one for Jackie Si Tou, UN Trade and Development, and also Linet Kwamboka from Global Partnership for Sustainable Development Data, what are the pathways for AI innovation that are truly inclusive, and how can local communities be real beneficiaries of AI?
So I'll invite our panelists to please address this initial question. So can we go with Jackie Si Tou. He's online. Remotely.
>> JACKIE SI TOU: Just to double-check whether you can see my screen and hear me well?
>> VALERIA BETANCOURT: Yes.
>> JACKIE SI TOU: Perfect. So my sharing will be based on this publication that was just released two months ago on the title inclusive AI for development.
So I think it fit into the discussion very well. To begin with, I would like to highlight three key drivers of AI of that case. They are infrastructure, data, and skills. And we want to look into the questions of [?] we need to focus on these three key elements.
When we can see a significant AI divide, for example, in times of infrastructure, one single company, Nvidia, actually purchased 90% of the GPU which is a critical component for computing resources.
And we witness the same kind of AI divide in data, skills, and also other areas like R&D, patent, scientific publication on AI infrastructure.
So this is the mainframe work that help to us dive into the discussion on how to make AI inclusive.
And the first message that I have is on the key take way to promote inclusive AI adoption. This is in our report on accessible AI adoption cases in developing countries. And based on the framework that I just shared on infrastructure, one very important takeaway is to work on the locally available digital infrastructure.
Right now over the world we still have one-third of the publication -- population without access to the Internet. So some kind of AI solution that is able to work offline would be essential for us to promote its adoption. That's what I meant by working with the locally available infrastructure.
And the second point on data, is to work with community leader data and also Indigenous knowledge. So we can really focus on the specific [?] and in the local context.
And the third takeaway is the skills that I mentioned. We use simple interface that help user to use all these AI solution.
And the last one is on partnership. Because from what we are investigate, many of this AI adoption at the local level is what is more scale. And to scale them up, we need to build better partnership to get access to the essential resources and technical support.
And the second message that I have this on the worker-centric approach of AI adoption.
From the period of technological evolution, we understand there are four key channels where AI may also impact this productivity and workforce. On the left-hand side we have -- on the top left starting with the automation forces that AI could substitute human labour. And on the top right-hand side we have AI complementing human labour. And the other two are deepening automation and creating new form of jobs. And from the previous experience, automation or this technology adoption focus on the left two, that is replacing human labour.
But if we really want to have an inclusive AI adoption that benefits everyone, we should focus on the right-hand side, on how AI can complement human labour and creating meaningful new jobs.
And with that, we need to focus on three areas of action. The first one is of course empowering the workforce. That includes digital literacy to reskilling and upskilling. So to make them adopting to this new AI approach of our work purpose.
And the second very important point is what I also mentioned before with engagement with the worker. So we work with the community, we work with the workers, with the design of implementation of AI to make sure it fit the purpose and also gain the trust of this whole AI adoption process.
And the last point is about fostering the development of human-centric AI solutions that would be the major responsibility of the government through our [?] procurement and tax and credit incentive that scale this AI adoption to work in worker-centric approach.
The last thing I want to highlight is at the global level there are four key areas we can work on. Accountability to scale what we want to advocate here is to have public disclosure of mechanisms that could reference the ESG framework that's really mature in the private sector.
An AI equivalent could happen with public disclosure on how this AI works and this potential impacts. So this is the accountability period.
And second one is on digital infrastructure to provide equitable access to AI infrastructure, a very useful model that we can learn from is the CERN model which is right here at Geneva that I'm working at.
And that model could help pull resources to help share the infrastructure for every stakeholder.
And the third one is on open innovation, including open data and open source that can really democratize our resources for AI innovation.
What we need is to coordinate all these resources for better sharing and better standards.
And the last point that I want to highlight is on capacity building. We think that an AI-focused centre and network modelled after the UN climate technology centre and network could help in this regard to provide the necessary technology support and capacity building to developing countries.
And of course the South-South Cooperation could help us address common challenges if the like in East Africa, we that I not have enough data source to change AI with the local language of Swahili, but putting the East Africa countries together, then we can put the Swahili common language in the region to have better AI training.
So this is some recommendations that I have and I'm happy to engage in further discussion.
Thank you.
>> VALERIA BETANCOURT: Thank you. Thank you very much, Jackie. So obviously multidimensional approach is needed for the [?] of AI to be distributed equally.
With that, I'd like to give the floor to Linet, global partnership for sustainable development data to also help us -- not here? Okay.
So is anyone in the panel willing to contribute to this part of the conversation in relation to how to bring the benefits of AI to local communities before we move to the other round? Okay, if not, we can check whether there are any reaction from the remote participants. Any questions in relation to this point or from here from the audience? You are also welcome to comment and provide your viewpoint.
Okay. If not, we can move to the second round which is going to look at Indigeneity. What radical shifts do we need in AI infrastructures for an economy and society attentive and accountable to the people? And I will invite ambassador of India to comment and Sarah Nicole from Project Liberty Institute to also help us to address this dimension of local AI. Please.
>> ABISHEK SINGH: Thank you for convening this and bringing this very, very important subject how do we balance between wide AI adoption, building models, applications, vis a vis the energy challenges that we have which hampers our goals toward sustainability development that we agreed long back.
It's not an easy challenge for government across the one hand. We want to take advantage of the benefits that are going to come. And the other hand we want to limit the risks that are coming on climate change and sustainable development.
So the approach towards local AI seems to be good, but to make that happen, there is several necessary ingredients to that. Many of it was highlighted by our speaker from UNCTAD very succinctly, but I would like to mention that what we observed in India, given the diversity that we have and the languages that we have, diversity culture and diversity that we have is microcosm of the whole world. How do we ensure that whatever we build in a country of our size and magnitude is -- applies to all sections of society. Everybody becomes included in that.
So in that one key challenge, of course, infrastructure with the AI compute infrastructure is expensive, it's not easy to get. Very few companies control it. Do that, if you're democratize infrastructure, the model we adopted in India was to ensure that we create a common central facility through which of course provided from private society, it should be set up with those doing training models are influencing or building applications.
And this compute, it becomes at a relevant and affordable cost. We underwrite to the tune of 40% of the compute cost from the side of the government. So end users gets it at a rate which is less than a, that per GPU per hour. This model has worked, and I do believe the solution that was proposed earlier, building a CERN for AI. If we can create a global compute secure facility across countries, foundations, multiple bodies joining in and creating this infrastructure and available, it can really resolve the access to infrastructure that we have.
The next is about data. How do we make sure we have data models available? Until we have necessary data in all languages and context, all cultures, it will not really happen. Do that, again, going back to the example we have in India, is that they also realized that we have lots of datasets in English and we have different languages. When it came to minor languages, we have limited datasets.
We launched a crowdsourcing campaign to get linguistic data across languages, cultures, in which people could kind of come to a portal and contribute datasets. So that has really helped.
So that model can, again, be kind of made global and different communities, even smaller communities can ensure that they can augment the datasets on which LLMs are trained which has the contextual and linguistic datasets that can be an innovative solution towards making the datasets more inclusive and more global.
The third thing on which we need to lean to push AI is about capacity building and skills. AI talent is rare and scarce. There are few countries which are well ahead in that. But in order to ensure that we impart necessary skilling and training to students and to AI entrepreneurs with regard to how to train models, how to wire up even thousand GPUs, it requires skills.
If we can take up capacity building initiative driven by a gentle initiative through the UN body or the Global Partnership on AI and ensure that all those talented people who have the capacity to build AI applications are given the necessary skills and training and doing inferencing and building models to solve problems, it can really help.
The fourth approach is build use cases. AI use cases in key sectors, whether it's health care, agriculture, education, and create a global repository of AI applications which can be shareable across geographies. If you're able to take these steps across the infrastructure, datasets, training, capacity building and building use cases, repository use cases, I think we'll be able to push forward the agenda of adoption of AI and building local AI at some stage.
>> VALERIA BETANCOURT: Also the AI models have to reflect contextually grounded ethics.
Then I'd like to invite Sarah Nicole from Project Liberty Institute. Please share your insights on this issue.
>> SARAH NICOLE: Thank you very much to give this short lightning talk and thank you for the first insight as well.
I will be a little bit controversial and really appreciate the way the question was framed. So I really appreciate the radicality aspect of it. Because the mainstream view is that AI is a completely disruptive technology that it changes everything in our societies, in our economies, in our daily life.
But I would argue quite the contrary. AI is essentially a neural network, right, that replicates the way the brain works. It analyzes specific datasets, it finds date patterns and uses those patterns to respond to prompt, search, so on.
Overall, it's an automation tool. It's a tool that accelerates and amplifies everything that we know. Necessarily the current structure that is highly centralized and that strips users' data out of their control and is reinforced by AI. And it also reinforced Big Tech companies and everything that we've been knowing for decades.
It benefits from the centralization of the digital economy that is necessary to train its model. So AI is very much so the result of the digital economy that has been in place for multiple, movement years. If AI is a continuity, and an amplification of what we already know, than the radicality needs to come from the response that will bring to it.
And at Project Liberty Institute, we believe that every people, users, citizens, call it what you want, deserves to have a voice, a choice, and stake in their digital life. And this goes first by giving users data agency.
This requires infrastructure design changes, profound one. In digital economy that is not just a byproduct, it is a political, social, and economic power that is deeply tied to our identities.
And most of the network infrastructure that is currently in place has been captured by a few dominate tech platforms, necessarily everything that is built under -- falls under this proprietary realm.
Scrapping, of course, empowerment of users, transparency, and so on. So we need to rethink this infrastructure model, because it shapes data agency. Anita, you've been great to launch this report with us in Berlin last month, so I'll be happy to share also this report that we wrote for policymakers specifically to equip them with thinking how to approach this digital infrastructure questions.
But infrastructure for agency is really what we're focusing on at the institute. So we are sort of an open-source protocol called a DSNP. It builds directly on IP. It allows users better control of their own data by allowing them to interact with a global, open social graph.
What this means is that your social identity on the DSNP is not tied to one specific platform like it is today in most tech platforms, but it exists independently. It allows transportability of your data, but also interpretability.
And as this protocol that has almost 2 million users now, this represents a radical shift for an economy and society attentive and accountable to the people.
Unfortunately, this would be a little bit too good to be true if all that was needed was a few lines of code and some spec and protocols.
As important as the business model, and there's a lot of work to be done here because to this day the most lucrative business model is the one that scraps data -- users' data and use it for advertisement. And we have yet to find a scalable alternative to this.
And in order to build what we call the fair data economy, we're in need of metrics. We need to be better at articulating what we mean by safety, responsibility, privacy, with what exactly do we mean behind this beautiful world.
We need qualitative behind this.
We need to shape a vision of technology that is socially and financially benefiting everyone.
And one of the approaches that we're exploring at the institute is the one of data cooperative. The cooperative model has a legacy of a hundreds of years and it's actually pretty well -- pretty well fit for the age of AI. We have a report on this coming out on July 9th and I'll be able to share it with those who wanted to.
But let me extract to points from this report that I think is interesting for the sake of this discussion.
Data cooperative allows to us rethink the value of data in a collective manner. I think that's very important, because the debate is very much structured around personal data and individual data. But the issue is so structural that we need to empower users with collective bargaining tools against those Big Tech corporation.
And the second point is in the age of AI, data needs to be of high quality and data cooperative provide the right incentive for data contributor to improve the quality of their data because then it contributes to greater financial sustainability of their own co-op. It's also for data pulling purposes.
And of course, there are many other models that exist, data commons, data trust, you name it, a radical shift for a better economy anyway will need many stakeholders to be involved and we're already seeing this every day in multiple communities across the world.
One last thing that I wanted to mention today is what I just said I don't think this should be considered as radical at all. We own our identity in the analog world. We don't expect others to make billions on top of our identity. So why should it be different in they line world.
All in all, the goal is to have a voice and choice and stake online. And I don't think this is radical, it's common sense. Thanks.
>> VALERIA BETANCOURT: Thank you. I think you helped us pave the way to the next round of conversation. We want AI to be meaningful to people, the intention behind it is absolutely crucial. And with that, I would like to invite ambassador Thomas Schneider from the government of Switzerland and Nandini Chami from IT for Change to address the question on how should AI innovation pathways be steered from the common good?
With that intention of the common good, share your views on that. Ambassador, welcome.
>> THOMAS SCHNEIDER: Thank you. And thank you for making me part of this discussion because this is a discussion of fundamental importance that's also something that maybe not in a necessarily poor but a small country like mine is a big issue. These scaling effects that we're seeing and you've highlighted some of the aspects, how can a small actor cope, survive, call it whatever you want, in such a system whereby design the big ones have the resources, the power.
But the question is does it have to be like this are so it just -- would there be alternatives. I think we have already heard a number of elements where you actually can -- how would the small ones need to cooperate in order to benefit from this as well. And of course we know about the risks and all of this.
But I think it would be a mistake not to use these technologies, because the potential is huge.
And being an economist and a historian and not a lawyer, actually, much of this reminds me of -- of the first revolution and the industrial revolution reference in Switzerland was a country that was lagging behind. They had already trains and railways in the UK and we were still walking around in the mountains.
But then we were catching up quite quickly, but it incumbent just enough to buy locomotives and coaches from the UK and produce them ourselves. We had to realize you needed to build an ecosystem in order to allow you to use this technology to make it your own. And some of it has been mentioned.
What struck me lately I read an article about the extinguishing of the Credit Suisse, of the Swiss bank. This bank was created by the politician and his people that were actually bringing the railways to Switzerland and building the railway system.
What they did, they did not just buy coaches and build railways and bridges and tunnels, they also built the 88 Zurich. So they knew we needed engineers and people to have the skills to drive these things, built the infrastructure. So they did not just create the railway, they created the first universities like in Polytechnic universities. We need somebody that gives us credit and we need a financial system around it.
You can have nice ideas, but if you don't get the resources for them, nothing happens. That was remarkable that this was all through one person plus his team in the 1840s and '50s.
And I think we -- we need to understand and I think we've heard a lot of it, but what do we need? Each community for herself, but also in order to be able to create our own ecosystem and how to cooperate with others that are in the same situation, it can be communities in the different country, but can actually be communities at the other end of the world, but that may actually create a win/win situation with you.
So I think this is really important. And for the small actors, how can we -- how can we break this vicious cycle of scaling effects that you cannot deliver. And we've heard also some elements that are important for us in Switzerland, the cooperative model is actually something much of our success stories economically are actually still cooperative.
The biggest supermarket in Switzerland was created a hundred years ago as a cooperative. It is still a cooperative, not as much as it used to be, but legally it's a cooperative. Every customer can actually vote. So every few years there's a discussion, should this supermarket be able to sell alcohol or not, and they want to but the people say no. So just one.
And we have insurances that are cooperatives and so on. So that's an element.
And another element is sharing the computing power. In Switzerland we've been working with Nvidia to develop the chips ten years ago and now we have the result. We have one of the ten biggest super computers, apart from the private ones with the big companies. In Switzerland. We try to correct a network, we've started to set up a network to share computing power across the world for small actors, universities, so on. This is called ICAIN, i-c-a-i-n. There's nice things do. If we do a nice summary of the things we've heard so far, we can actually, yeah, that gives us guidance for the next steps.
>> VALERIA BETANCOURT: Thank you, ambassador.
Nandini, please.
>> NANDINI CHAMI: It's a very interesting conversation and I think we're having this at a very timely moment when there was a recognition that if we are talking about a just and sustainable transition, we need to get out of the dominate AI paradigm and move towards something else.
So I'll just begin by sharing a couple of thoughts about challenges that we face in terms of steering innovation pathways for the common good. And these reflections come from the UNDP's human development report of 2025.
It focuses on the theme people and possibilities in the age of AI.
So the first challenge that, you know, in this report we find is that in terms of shaping the trajectories of AI innovation, private value and public value creation goals are not always necessarily or automatically aligned. And to quote from a quote, despite AI's potential to accelerate intellectual progress and scientific discovery, they're geared towards deployment, scale, and automation often at the expense of transparency, fairness, and social inclusion.
So how do we shape these with intentionality and consciously, that's very important.
The second cite from this report is that since development is a part dependent project, that's part dependencies does not mean that they automatic open up route to diversification. We just heard discussion on ecosystem strengthening and this report adds to the similar lens that the economic structures in many developing countries and LDCs may limit the local economy's potential to absorb product activities from AI, and that maybe fewer and weaker links to high-value added activity.
So this actually means that there needs to be a complementary between development roadmaps and AI roadmaps. And the objectives of development, the specific context will strengthen opportunities, challenges and weaknesses mapping in terms of where the potential for economic diversification lies.
And there we use AI as bridge building as a gender purpose technology, these become extremely context, really grounded activity to do. And we need to move beyond an obsession with AI economy roadmap as a technology activity and look at it as an ecosystem activity.
So from this perspective, I would just like to share some of our work at IT for Change. About three to four reflections on what it would be to make techno institutional choices that would shape these choices in the directions that we see.
So first, we come to the issue of technology foresight. And if the panel also we were discussing the question of do no harm principle. Oftentimes in these debates, we hear a discourse of inevitability of AI as a Frankenstein technology that will just definitely go out of control and there's a lot of long-termists, alarmists about we will no longer be able to control AI.
But what happens is this starts distracting from setting limit on AI development in the here and now. Which actually means that in operationalizing and actionizing the do no harm principle, instead of moving fast and breaking things, we probably need to go back to the precautionary principle of the declaration about what we need to do to shape technologies.
And secondly, as was mentioned on matters on the context of decision-making, we need to talk about the right of the public to participate in AI decision-making. So we're not just looking at rights of affected parties in the AI harm's discourse.
The second point is that in AI value chains which are transnational, which are very complex and which have multiple actors and system providers and deployers and subject citizens on whom AI is finally deployed, how do we fix it for societal arms and how do we update our product for liability regimes so that the burden of proof is no longer on the party to prove the causal link for the harm that was given the black box nature of this technology. Thinking this through becomes very important.
And thirdly, when we look at the technological infrastructure choices, of course open AI affordances become very important as a starting point. But it's also useful to remember that they don't automatically enable better accountability, democratic competition, and inclusivity as experiences of how we build open source AI on top of DPT stacks have shown. That it's very much possible that a Big Tech firms, dominate firms are able to use AI development to improve their products rather than to bring democratic sources as it shows.
And my last is about policy support for alternatives, particularly federated AI common thinking. So that alternative visions such as community AI that focus on looking at task-specific, small solution has may not use so much compute at scale, but will run very contextually grounded instances in specific communities that IT for Change we're exploring the development of such a model that public school education system and [?] for instance that have also been proposals that have been made in G20 discussions as part of the G20 dialogues about how do they shape public procurement policies and the directions of public funding for development of shared compute infrastructure which came up in our discussion.
And also how do we ensure that in the participation of different market actors on public AI stacks and the use of public AI compute, what would be licensing safeguards and other things that we built.
And the macroeconomic connections between the rules that we are signing in digital trade agreements and the policy space in the development country context to provide the necessary scaffolding to building a diverse and decentralizing economy ecosystem, we need to think about that.
>> VALERIA BETANCOURT: Thank you. Thank you so much. Let me check with Sadhana if there is anyone that have any online participants with questions. I invite you to get ready with you questions if you have them.
>> SADHANA SANJAY: Thank you. I hope everyone can hear me. There's one question in the chat from Timothy who asks digital transformation is built upon Intellectual Property Rights frameworks, means of ownership and trade. When considering existing trends, projects and works that are resourced versus those that lack resourcing, how are natural legal persons provided the necessary support to retain legal agency, both for themselves and as to support traditional roles, such as those of a parental guardian and others.
>> VALERIA BETANCOURT: Thank you. Anyone would like to address that question?
>> SADHANA SANJAY: If I understand correctly, given that there are ownership rights conferred on the developers of AI and nonnatural legal persons such as corporations. The question is about how can natural legal persons such as ourselves retain our rights and agencies over the building blocks of AI both individually as well as those who might be in charge of us such as guardians and custodians.
>> ABISHEK SINGH: I think one part is that of course the way technology is evolving, there is IP-driven solutions and there are open-source solutions.
So what we need to emphasize is to -- is to promote open-source solutions to the extent possible so that more and more developers get access to the APIs and they can build applications on top of it.
The second part of it is that with regard to, like, [?] like it's not that everything will come for free and those companies which are known to provide services for free, they monetize your data, we all know about it. There are companies where they're doing that.
So at some point in time we'll have to take a call whether if I want to use a service like you mentioned the ChatGPT service which helps me in improving my efficiency, my productivity, either pay for the service or I contribute to the research.
That all the individuals, companies, societies will need to take, what is the cost of convenience and what is the cost of getting a service and what form we can do.
The other part which can be done which is very complex is work out where every service is priced. If you have contributing datasets and building datasets in a particular language, can we incentivize those who are building that. There's a company in India that is doing that, it's paying people for contributing to datasets, which kind of ensures that they become part of the ecosystem they do that.
Then there are companies which started incentivizing full [?] and cab drivers, Uber drivers so that when they drove around, they get details about city amenities, garbage dumps, traffic lights, et cetera, sharing that information with the city government and then they get in turn paid for doing that service.
The way the data contributed, in what form is it contributing. There can be models and mechanism which a cost initiative model can be developed. But it will require specific approaches to the specific use case. But it's not that it can't be done.
>> VALERIA BETANCOURT: Thank you, ambassador.
>> THOMAS SCHNEIDER: Maybe if I can add, there's a number of -- first of all, property rights are not cast in stone. This is something that can be and needs to be reformed, renegotiated, I think that is the outcome and how this is another question, because otherwise in many ways property rights don't work also for journalism, for media, and that part. We'll have to develop a new approach and question what was the original idea behind property rights.
The idea may be right, but then we need to find a new approach. That's one element.
And the other thing, on the political level and market level, try to find ways to create a fair share system for benefits and one is try to monetize it like kind of transaction, give every transaction or every data transaction a value.
And the other thing I think we've already heard is go for -- not thinking only from the individual, but think is from society or from -- and in Switzerland also we have many -- we are liberal country, but many things people don't want it to be privatized because they think this should be in public hands. It's like waste management or hospitals is a very hot issue.
So I think we should think about how as a society if you want to develop a health system, for instance, health data, this is super important, it's super valuable. And of course the industry needs a lot of money to develop new pharmaceutical products.
But how can we organize ourselves as a society not because as individuals we too are weak. But a whole society can say we are offering something to businesses that can develop stuff that it's okay that they make money, but we want somehow a fair share of this because we are kind of your research lab and then you -- if you are a group, a big group, then you can have also political weight.
Then you need to find creative, concrete ways to actually then get this thing concretely done.
But you need to work on the idea, the concept, and on defining ways. But it's a super important question.
>> SARAH NICOLE: If I can build on those two, I fully agree with what's been said. The question of having a stake in your data is framed on a personal level. And actual studies have shown that you would make very, very little if you were to monetize your own data. At the end of the year it would be couple hundreds of euro -- or dollars. And the worst thing is that it could lead to us systems where poor people would probably spend lots of time online to generate very small revenue from this.
So answer will not be on an individual perspective, but it would be an a collective one. Because it's when the data is aggregated, when it's in a specific context that it then gains value.
And here again, let me bring the cooperative model. And that's true, there's a lot of work on data cooperative, practically speaking it's still yet to emerge. But also one of the reasons is that it is not natural for businesses to turn into a cooperative model because it's being perceived as this socialist or communist thing. Which is not -- and hundreds of years I think have proven that.
But there are many cooperative data cooperative that pull specific data with specific type of expertise. And then allow some AI to be trained on this expertise and high-quality data.
And this is where we can -- we can have a better rights and better protections for individuals once it is aggregated in common. So really the mentality needs to shift from this personal data framed discussion, I think that benefits also a lot of the Big Tech companies, to a more collective and organization perspective.
>> VALERIA BETANCOURT: Anita.
>> ANITA GURUMURTHY: I don't think there's an easy answer, and I think we need to step up and rethink, as people have said, on this idea of what's ownership.
Two things I would like to say is that for developing countries, particularly I think in our global agreements on trade and intellectual property, we oftentimes see our space to regulate in the public interest back in our countries. So often transnational companies use the excuse of trade secrets to lock up data that otherwise should be available to public transportation authorities, public hospitals, et cetera.
And perhaps we do need to strongly institute exceptions in IP laws for the sake of society to be able to use that threshold of aggregate data that is necessary to keep our societies in order. I'm sorry I'm using that terminology in a very, very broad sense.
But I mean, that is needed. You can't lock that up data and say it's not available because it's a trade secret.
The second thing is for the largest source, especially ChatGPT, was Wikipedia. So you see free writing happening, you know, on top of these commons. And therefore it's imperative for us to rethink the regime, well, we'll do open-source, but what if my open-source is for profiteering.
So we do need to protect society from free writing and also foul dealing. Foul dealing is when, you know, the exploitation reaches a very, very high threshold.
The last point I want to make is we've been talking about the economy that's generated the datasets. But what we read today is there's an economy of prompt.
On top of AI models that, you know, you see when you search, is the way in which you're defining your prompts as users. And that is perfecting the Large Language Models.
So this is a complexity from [?] to prompt which means that all of us are feeding the already monopolistic models with the necessary information for that to become more efficient. Which effectively means that the small can never survive. What do you do so the small can survive to this economy of prompt and the economy of profiteering from prompt can actually be curtailed. I think these are future questions for governance and regulation.
But essentially also for international cooperation.
>> VALERIA BETANCOURT: That's excellent. Let me now invite your comment please, or your question.
>> I'm Dr. [?] it's a consultative state at the UN. And by accident I'm expert intellectual property. I want to comment about the intellectual property of AI. In the wider international organization of intellectual property, they not yet reach to the ideal convention for protecting AI because it's divided between two sections. The AI is a data, a technology way. And the content which is generated by AI.
But for this, we are at a Civil Society have launched in the IGF last IGF in Riyadh a platform for protecting the content for users of digital era.
When the users want to share their articles, researchers, photos, trademark and they don't know where to [?] this, we make a platform to submit and take a QR Code and verified by Blockchain until he go to the government minister which is responsible for registration.
But this verification is very important to prove that priority date of sharing. This is in the international law of care of intellectual property is taking in consideration in the court in the case of conflict between users.
That's just a comment for the question they make. Thank you.
>> VALERIA BETANCOURT: Thank you for your input. We are a minute away from the end of the session. I would like to invite every one of you on the panel to share some final remarks.
Yes.
>> Just to add something, anyone want to know about this platform, it's www.ipverification.org. It's available now as a demo.
>> VALERIA BETANCOURT: Thank you very much. Thank you.
Just very brief final remarks like ten seconds with the highlight that you would like to leave the audience with, please.
Let me start with you, Nandini.
>> NANDINI CHAMI: I think the discussion is showing us that there's a long history of contestations about the public domain and how to draw the line between private property and public domain while incentivizing innovation and preserve common heritage, particularly in knowledge IP. AI is a new infestation of that problem.
>> VALERIA BETANCOURT: Ambassador Schneider.
>> THOMAS SCHNEIDER: I though was exciting and I hope we can follow up on this and I thank you for this discussion.
>> VALERIA BETANCOURT: Sarah.
>> SARAH NICOLE: Thank you will be the last thing I'll say.
>> ABISHEK SINGH: The cooperative model for infrastructure and datasets work and maybe for models and applications we need to push forward for those models without the concerns of IP and other stuff.
>> VALERIA BETANCOURT: Absolutely.
>> ANITA GURUMURTHY: I'm thinking that the public cannot exist without each other.
>> VALERIA BETANCOURT: Yeah, absolutely. Absolutely. And I thank you so much for your presence. And note the answers, as you said -- yes, I'm sorry, Jackie, please, your final remarks.
>> JACKIE SI TOU: Yes, thank you.
I think data is very strategic and key for both AI and the digital economy. With that I want to share with you that we have ways to establish a multi-stakeholder working group on data governance. Hopeful that I that could provide some recommendation on how we can get good governance framework.
Thank you.
>> VALERIA BETANCOURT: Absolutely. You have the answers, some of the responses and solutions coming from the margins, from the academia, from the social movements and different groups imparted by digitalization. Let's keep the conversation going in this space in order to be able to define the grounds for different approaches and different paradigm for AI for the common good.
Thank you so much for your presence and to all of your for your contributions.
(Applause)
