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[00:00:09] So welcome to the Global Network webinars. My name is Alexander Loshki and I will be moderating this webinar today together with my colleague Sean Lovell. Today, we have with us Kala Yem Amesaga from the
[00:00:27] World Economic Forum, who will speak about AI governance and data sovereignty. This is the third of five webinars in our AI for Official Statistics series. As AI reshapes economies, institutions, and societies,
[00:00:48] data sovereignty is emerging as a practical governance challenge rather than a purely legal or geopolitical concern. This webinar will examine how sovereignty should be understood, not as isolation or self-sufficiency, but as strategic control, resilience,
[00:01:15] and trusted in the and interdependence across the data and AI value chain. Moving beyond narrow debates on localization, the session will explore why sovereignty must reflect the differentiated capacities of states, cities, communities, platforms and multilateral actors.
[00:01:39] With AI agents and cross-border data use rising. Raising new questions of confidentiality, equity, and control, the discussion will focus on governance models, interoperable standards, and regional collaboration that enable innovation while safeguard agency.
[00:02:00] Trust and shared value.
[00:02:04] So let me introduce now our speaker. Carla is Lead of AI and Data Governance at the World Economic Forum. She's working on topics such as AIAI agents governance, data sovereignty, and data equity. She's passionate about exploring the ways in which data, AI,
[00:02:27] and technology can promote digital transformation of governments, public-private collaboration, and inclusive development around the world.
[00:02:38] Prior to joining the World Economic Forum, she worked at the Inter-American Development Bank, supporting digital government and statistical capacity building projects. Before that, she worked also at the World Bank, OECD.
[00:02:55] Ashoka Changemakers and the Mexican Ministry of Foreign Affairs. Carla holds a master's degree in international affairs from the University of California in San Diego and a bachelor's degree in political science
[00:03:11] and in international relations from the Autonomous Technological Institute. of Mexico.
[00:03:21] So just before I hand over to Carla, just some practical remarks, as always. Please write your comments and questions into the meeting chat. You can do that at any time. And then during the Q&A session,
[00:03:37] we would encourage you to ask the question yourself if you can. Otherwise, we will read the question out for you.
[00:03:47] As always, this Global Network webinar is being recorded and will be made available on the Global Network of Data Offices and Statisticians on LinkedIn. And you will find the link to join this LinkedIn group in the meeting chat.
[00:04:04] We also invite you to follow the link to subscribe to our monthly newsletter. And that would be everything from my side. So now over to you, Carla. The floor is all yours. Thank you.
[00:04:21] Thank you so much, Alexander, Sean, and thank you to the Global Network of Data Officers and statisticians for the invitation. And of course, thank you all for joining today. Good morning, good afternoon,
[00:04:33] and good evening, wherever you are. Know that this is a complex time for the data and official statistics community. You've been asked to produce faster, more trusted, and more granular statistics. You're also being asked to work with new
[00:04:48] data sources, new technologies, and new institutional expectations. sometimes with budget constraints. And that was already true over eight years ago when I was at the Inter-American Development Bank,
[00:05:02] working closely with NSOs on capacity building and digital transformation. And it is even more true today as AI is moving from experimentation into core institutional workflows. And at the same time, more data is being collected, processed, and reused by private actors, platforms,
[00:05:20] and automated systems, requiring even more cross-sector collaboration. So AI governance and data sovereignty have become key practical questions for public institutions. And this is a topic that we're going to discuss today.
[00:05:35] Now I'm going to start sharing my slides. So bear with me for a second.
[00:05:50] The ideas that I will share with you today are based on the work carried out at the World Economic Forum. including through our Center for AI Excellence. And this community brings over 600 high-level experts and innovators to
[00:06:05] drive AI and data usage in ways that foster innovation, economic transformation, and human-centric outcomes.
[00:06:14] This work is also derived from the insights from our experts that are part of the Global Future Council Network, in particular the Global Future Council on Data Frontiers. This is part of the network of our 700
[00:06:27] experts from all sectors in all parts of the world. And this council seek to advance ideas for innovative and positive impact in the world. During my presentation, I will also pose some questions for self-reflection that will help us explore some of the concepts being discussed.
[00:06:48] And throughout the call, I invite all of you to share examples, tools, case studies, research, or any reactions that you have on the topic presented on data sovereignty and AI governance more broadly.
[00:07:00] We would love to hear from you. And we will also have some time at the end of the the presentation for questions and answers.
[00:07:12] So today I will focus on 7 key themes, why data sovereignty matters now, how data sovereignty is becoming operational, why sovereignty is not the same as localization, why the sovereignty landscape has been changing and now it's heterogeneous,
[00:07:32] why we need to move from ownership to operational rights, how AI agents make sovereignty more urgent, and what trusted interdependence AI infrastructure And digital embassies mean for statistical offices.
[00:07:49] First, let's start with setting the baseline for our conversation. Today we'll be talking about data sovereignty, not from a legal term per se, or not from the geopolitical angle, but more from a practical governance question.
[00:08:06] When data is collected, shared, processed, used to train or query AI systems, or acted on by AI agents who has meaningful control. And please allow me to emphasize on the word meaningful because this really
[00:08:20] matters. A government may say they die sovereign because it is stored luckily. A company may market a product as sovereign because the cloud region is domestic. Or a regional block may use sovereignty
[00:08:33] as a strategy for competitiveness or autonomy. And these three claims may all be valid in part, but none is sufficient on its own. So today I want us to explore together what sovereignty looks like in practice.
[00:08:51] First, let me begin with a complex yet increasing common scenario that you might be experiencing. Imagine a national statistical office preparing a labor market estimate. The workflow uses survey data, admin tax data,
[00:09:07] private sector job posting data, and machine learning model hosted through a cloud provider.
[00:09:16] Then, on the other hand, you have a GenAI assistant helping analysts drop documentation, or maybe an AI tool checking anomalies in the data and doing quality control. Some data states in the country, some metadata or data crosses borders,
[00:09:33] and maybe there's some even some processing happening to a trusted provider. And then the outputs of this analysis are maybe reused by another agency in another country. So now ask yourself, where is the data?
[00:09:49] After all this back and forth and all these changes, where's the data? And this is a key question. But this question by itself is no longer enough. We also need to ask ourselves, who approved the data use?
[00:10:06] What model uses data? What agent. To net. What metadata follow it? Can the statistical office audit the process later? Can it stop, correct, or revoke downstream use? and the people, businesses and communities represented
[00:10:25] benefit from the value created.
[00:10:31] These are not straightforward or easy questions to answer sometimes, but these are important and kind of like give us an insight of where the conversation has been shifting.
[00:10:43] Because we're talking about a shift in perspective. If sovereignty is not only about where data sits, but about who can govern, what happens to it, then the first practical question would be, how is data sovereignty changing as AI
[00:10:58] becomes part of everyday institutional work? And that brings us to the main first point, data sovereignty is becoming operational.
[00:11:09] That is no longer simply collected, stored, analyzed, and published. It is increasingly used to train models, fine-tune systems, generate synthetic data sets, power AI assistance, support automated workflows, and informed decisions in real time.
[00:11:28] So the core question is shifting. It is no longer is the data inside our borders. It is also, can we govern what happens to the data across its full life cycle?
[00:11:41] And for statistical offices, this life cycle is familiar. It includes collection, cleaning, linkage, classification, analysis, quality insurance, assurance, dissemination, archiving, and reuse. But AI adds new layers around model training, prompt logs,
[00:12:03] synthetic data generation, automated coding, and agentic workflows.
[00:12:12] And as we explore these new implications that are brought by AI, we find ourselves in a position where there's no necessarily straightforward definition of data sovereignty. At the forum, we've been exploring with our experts,
[00:12:32] like what are the different angles of data sovereignty in the context of AI. And what we found is that there's a joint definition that kind of like touches on concepts like governance, values, control, flexibility,
[00:12:47] resilience, localized investment, but also trusted collaboration, autonomy, and international cooperation. In some of these concepts embedded in data sovereignty, first talk about sovereignty in the form of capacity and not necessarily just
[00:13:08] formal authority. They also convey the idea that sovereignty is not isolation. A country can keep data inside its borders and still lack control if compute, model updates, encryption keys, or access controls sit elsewhere.
[00:13:27] And 3rd and very important is that sovereignty must support interoperability and trusted collaboration. For many countries, institutions, and communities, sovereignty will be achieved through well-governed interdependence and not
[00:13:41] through full self-sufficiency.
[00:13:46] So that's why sovereignty is increasingly a governance issue, not only a legal one.
[00:13:53] And it's about whether an institution can act, verify, correct, exit, and benefit from these data interactions.
[00:14:04] Now let's pause for a moment and try to internalize some of the concepts that we have discussed. And I invite you to think about one AI or data workflow in your institution. And if you had to explain who controls the data, the model, the compute, and the audit trail.
[00:14:25] Could you answer all of these questions confidently?
[00:14:41] Once we define sovereignty as operational capacity, one important implication follows, that we cannot really measure sovereignty only by looking at geography. Location matters, yes, but it's only one layer of control.
[00:14:56] A dataset might be stored locally while real authority sits elsewhere, either in the cloud architecture, the model provider, the encryption systems, the logs, or the vendor contract. So the next step is to separate sovereignty from one of its most common
[00:15:13] associations, localization.
[00:15:18] Sometimes localization is necessary, yes, for sensitive government data, national security data, health data, or any other confidential statistical microdata. But localization alone does not create sovereignty.
[00:15:35] A data center inside the country does not automatically answer the key questions. Who controls the hardware? Who operates the cloud? Who access the logs? Who audits the model? and what exit options exist if the provider relationship changes.
[00:15:51] So localization can move data without moving power.
[00:15:56] An assistant can look sovereign on a map and still be dependent in practice.
[00:16:01] And at the same time, data stored abroad is not automatically outside sovereign control. Strong legal safeguards, technical controls, audit rights, encryption, and trusted institutional arrangements can all support control across borders.
[00:16:20] So instead of treating sovereignty as a yes or no question, might be more useful to think in terms of a full stack. Instead of asking, is the data local? Perhaps we can ask, where does the authority actually sits?
[00:16:35] and where do we need control, where do we need partnerships, and what safeguards make that partnership trustworthy?
[00:16:45] And this is a moment also to reflect. For example, let's pause for a few seconds. Think of one technology vendor or technology platform your institution relies on.
[00:16:59] Now, ask yourself, would you answer who controls the encryption keys, the audit logs, the subcontractors, and exit process?
[00:17:11] Perhaps you can answer these questions, or perhaps you have not fully stopped to understand if you have like all the information needed from your either vendors or external providers. And these are the important questions to
[00:17:27] ask yourself as you are. either engaging in contracts as you are processing or handling data, especially if it has a sensitive nature.
[00:17:43] And this full stack view that I introduced also shows that data sovereignty is not held by 1 actor alone. Once we ask where authority actually sits, we see many layers and many actors involved. We see governments,
[00:17:57] statistical offices, cities, communities, tech providers, researchers, and regional institutions. which means that sovereignty is not uniform. It's different across actors and have different force and control,
[00:18:11] responsibilities and vulnerabilities.
[00:18:17] And that brings us to the broader sovereignty landscape and to the question of data equity.
[00:18:24] The concept of sovereignty naturally makes us think of states. And indeed, states are central to this conversation. They make the laws, negotiate the agreements, regulate the markets, invest in infrastructure, and set the national strategies.
[00:18:41] But in the data and AI era, sovereignty is shaped by many actors. by statistical offices because they're, and you are the trusted data stewards, setting standards, protecting confidentiality and deciding
[00:18:54] what evidence is produced and how it is communicated. But also other actors are very important cities as they collect different kinds of urban data, communities, indigenous communities, language communities, migrant communities, diasporas.
[00:19:15] Also, private platforms matter, hyperscalers, model providers, cloud vendors, data brokers. really shaping what is technically possible and commercially viable. And regional and multilateral institutions matter.
[00:19:29] They can support shared standards, regional collaboration, capacity building, and interoperability.
[00:19:38] So in this landscape with sovereignty is shared across various actors, we see that not every actor exercises control the same way. The national government may exercise sovereignty to regulation, procurement,
[00:19:56] and infrastructure investment. But the Statistical Office may exercise it through confidentiality protocols, access rules, metadata standards, and quality frameworks.
[00:20:10] And this is a great moment to introduce also why data equity has become essential, has always been important, but has become essential in this conversation.
[00:20:24] And what I mean by data equity is the shared responsibility for fair data practices that respect and protect human rights, opportunity, and dignity.
[00:20:34] And this concept is important in the conversation of data sovereignty because it makes us look at the full data life cycle, the collection, curation, processing, analysis, sharing, application, retention,
[00:20:48] reuse and disposal of the data.
[00:20:54] it makes us ask ourselves, like, who is visible in the data? Who's missing? Are the attributes used to describe people or communities accurate and appropriate? who can access the data, tools and benefits.
[00:21:09] and also who benefits from the data, who might be harmed or who may want to seek a remedy from any negative externality or any negative outcome.
[00:21:25] So groups that are underrepresented in data are often underserved in policy. So that's why this is a key consideration, especially for statistical offices and government offices, because poor quality data about our
[00:21:38] community can lead to poor quality decisions. about that community. And AI in the context that we're living in can also amplify these patterns, these biases, these gaps, if they're not addressed deliberatively.
[00:21:55] So sovereignty should not be reduced to state control over data. It should also include different agencies and different actors and help advance the ability of institutions, communities, and people to shape how data about them
[00:22:09] is used. how data is shared and how data is benefiting the communities that is coming from.
[00:22:20] So if sovereignty is shared across many actors as we saw, then the language of simple ownership quickly becomes insufficient. the practical question is no longer just who owns the data.
[00:22:35] But now, and especially for statistical offices, the more useful question could be, who has and which rights, under which or what conditions, and with what accountability.
[00:22:49] So we need to move from ownership as a broad claim to operational rights as a governance tool. Many data governance institution discussion still rely on the language of ownership. The language can be used in some context, but it's often too blunt for public
[00:23:05] sector data and official statistics.
[00:23:09] Can a business own its survey response once it is used for official statistics? Can a statistical office own admin data collected by another ministry? These questions become quickly complicated. So for a practical standpoint and practical definition of governance,
[00:23:28] it is often more useful to shift from ownership to operational rights and controls.
[00:23:36] So for any important data set or AI enabled workflow, we should ask questions like this instead. Who has the right to access this data? Who can approve a new use? Who can audit the system? Who can correct or challenge outputs?
[00:23:54] and who can exit or revoke any access.
[00:24:01] And for statistical offices, these questions are concrete and important. take administrative data. The issue is not whether a tax authority or health ministry shares data with the statistical office, but the greater issue is whether the statistical purpose is clear,
[00:24:18] quality limitations are documented, linkage is governed. Access is logged and the resulting statistics are trusted. And trust, as we know, is at the center of all data interactions and exchanges.
[00:24:38] So moving on. Sober team practice means being able to answer. questions like. What is this data? Where did it come from? What has been done to it? What rights has attached to it and what happens next?
[00:24:59] And this makes Providence. Traceability and. Central.
[00:25:05] A statistical and data offices already understand metadata better than. Any other institution?
[00:29:11] We should define roles, set permissions, explain escalation rules, monitor performance, and review access over time.
[00:29:21] Every significant AI agent. working in a statistical or government environment should have a clear authorization profile.
[00:29:30] related to the purpose, the data you can read, where actions are prohibited, where actions require a human in the loop, how lots are kept and such. And this is how institutions can preserve trust while using more capable tools.
[00:29:49] And this also connects directly to data sovereignty because if an agent can act on data, move data, transform it, and trigger decisions, then sovereignty depends on the controls that we set around that agent.
[00:30:07] let me go back to a bigger point that was made earlier, that control is essential, but control does not mean isolation.
[00:30:17] So even the strongest governance frameworks depend on infrastructure, such as compute, connectivity, storage, talent, cybersecurity, skills, vendors. So the next question becomes, especially in a global context,
[00:30:34] how can countries and institutions preserve agency while participating in an interconnected AI ecosystem? And this is where trusted independence becomes very relevant.
[00:30:51] So for many countries and institutions, isolation is neither realistic nor desirable. AI infrastructure is expensive, compute is concentrated, talent is unevenly distributed, and data standards vary.
[00:31:11] Also, many statistical challenges are cross-border by nature. Migration, climate, trade, health, digital platforms, supply chains, financial flows. So through research at the World Economic Forum, we have found that AI infrastructure can be built around 3 main building blocks,
[00:31:31] compute, which is data in use, connectivity, data in motion, and storage, data at rest.
[00:31:39] So to scale these building blocks, economies need technical prerequisites, energy, water, land, hardware, and cybersecurity. but they also need institutional prerequisites, policy, talent, and financing.
[00:31:56] And this is why AI sovereignty should be understood as a strategy and not just a purity test.
[00:32:04] And also because economies sit on a spectrum, at one end is extensive domestic ownership, where there's local control of compute, connectivity and storage. And at the other end, you find trusted international
[00:32:18] partnerships. such as shared infrastructure and governance with reliable partners.
[00:32:27] So we find that in practice there is. very few instances where, countries operate at the extremes, and actually most economies will use hybrid strategies. And this is where digital embassies concept becomes relevant.
[00:32:45] And this is not a new concept, but it's not well known. It's basically a secure digital space, just like a regular embassy, but think about it in the digital context. It's a secure digital space in a host country where a guest.
[00:33:02] country can, the guest countries' data, workloads, storage, and sometimes compute remains governed under the guest countries' own jurisdiction. So you can host data workloads. somewhere else, but they're still obeying your own
[00:33:21] jurisdiction. And here we're not just talking about placing data abroad. We're talking about extending sovereign control through legal, technical, and operational safeguards. And Stonia pioneered the concept by hosting government data and essential
[00:33:38] registries in Luxembourg to preserve continuity of core state functions if domestic systems were disrupted.
[00:33:47] And now the concept is re-emerging and gaining more traction in the AI infrastructure debate because many economies need access to sovereign government capacity before they can build everything domestically.
[00:34:00] And this is important because countries should not necessarily aim to develop the whole AI stack in-house, but can rely on collaboration and exchanges to do this. So digital embassies can help governments
[00:34:15] preserve continuity when domestic infrastructure is disrupted. expand access to computer storage when local buildout is low or expensive, keep data and operations subject to home country law, and scale capacity with demand rather than building large assets upfront.
[00:34:38] But digital embassies only work if trust is designed into the arrangement. And when we talk about trust, we mean there has to be political commitment and willingness, a clear legal basis and scope of the
[00:34:52] agreement, strong data management, technical safeguards, and operational rules.
[00:35:02] And these arrangements need to be safe and need to be audited. And they have to also envision scenarios where political conditions change and the data needs to be withdrawn.
[00:35:18] So the digital embassy can help countries strengthen sovereignty and collaboration, but only if it's legally robust, interoperable, auditable, encrypted, resilient, and portable.
[00:35:35] And this is an important lesson for official statistics, because trusted interdependence means investing domestically where control is essential, using shared or regional capacity where it is sensible, and designing every partnership so that
[00:35:49] accountability. Observability, exit, and benefit sharing are real.
[00:35:57] So taking all these together, these examples point to one conclusion that data sovereignty in the AI era is not a single policy choice or a single technical control or procurement clause. It's a set of capabilities that must be
[00:36:14] designed Tested and maintain across data models, infrastructure, vendors, communities, and institutions.
[00:36:23] So now please allow me to translate all of this into five practical actions to consider. First. Map the data and AI stack for critical statistical functions. For your most important workflows, know what data enters, where it is stored,
[00:36:41] where it moves, what compute processes it, what models or tools touch it, what vendors are involved, and what safeguards apply. Then apply data equity as part of the sovereignty governance. So ask who is represented, who is missing, who may be harmed, who benefits,
[00:36:59] and who has a voice.
[00:37:03] Third, make provenance and metadata non-negotiable. You should be able to trace not only a data set, but also major transformations, AI assisted steps, synthetic data generation, model versions, human approvals, agent actions, and downstream dissemination.
[00:37:24] Then create authorization profiles for AI agents in high impact tools. So be clear about what the tool can read, write, recommend, publish, or trigger. And always keep humans in the loop, especially where confidentiality, rights,
[00:37:39] public trust, or irreversible consequences are involved. And finally, test for sovereignty washing in procurement and partnerships. Make sure that true sovereignty is verifiable. And it's not just being said on paper.
[00:38:01] And always ask yourself some sanity questions. Can you export your data? Can you port your model? Can you rotate your own encryption keys? Can you access process and transfer events be independently verified?
[00:38:16] Is there a tested exit plan?
[00:38:22] So across these actions, there's a clear message that sovereignty becomes meaningful only when it can be exercised in practice.
[00:38:32] And to close, sorry, and to close, let me share 3 final takeaways. First, data sovereignty is agency, not isolation. It is the ability to shape and govern data use across the data and AI value chain. Second, sovereignty is layered and differentiated.
[00:38:52] So states matter, but so do statistical offices, cities, communities, platforms, researchers, and regional institutions. Each has a different layer and leverage. In this equation. And third, sovereignty must be operationalized.
[00:39:10] It becomes real to rides, metadata, provenance, auditability, authorization, interoperability, benefits sharing, technical safeguards. The next adoptions.
[00:39:25] So for the statistical community, this is not a departure from your mission. It's an extension of it.
[00:39:32] You've always depended on a social contract, working with people, businesses, institutions to provide data because they trust it and trust it will be used responsibly, confidentially, and for the public value.
[00:39:44] And AI now is just changing what institutions can do with data and also changing what and who participates in data value chains, who has visibility and who has leverage.
[00:39:56] So in this context, how do we all preserve agency, trust and share value in a world where data models and infrastructure and agents are increasingly interconnected?
[00:40:09] That's the practical challenge of data sovereignty in today's world.
[00:40:16] Thank you so much for listening. My contact information is on the screen. I'll also share some links in the chat. Please reach out to me if you want to learn more about this work. And I look forward to the discussion.
[00:40:33] Thanks so much, Carla, for these very, very useful insights on data sovereignty and what it entails and what should be included or taken into consideration when talking about data sovereignty and data governance. I would like
[00:40:51] Now, to invite our colleagues in this webinar, if you have questions, please feel free to now raise your hand and we will give you the floor to ask your question. You can also... Write your questions into the meeting chat and we can read it out to you,
[00:41:14] but I think it would be nice if you could maybe ask the question yourself. So I saw there was one. Question in the meeting chat, so I just go in order of the questions as they appear in the chat. So Jose Miguel Guzman,
[00:41:34] do you want to come in? Otherwise I can read it out.
[00:41:42] So, Jose, please go ahead.
[00:41:48] And you are muted, so if you want to talk, please unmute yourself.
[00:41:59] I see your hand, Jose, but if it does, now. Okay, I think, yes. Thank you very much. It was so incredible, amazing presentation, Carla. Thanks. You have show us so many concept ideas. It's like we need to
[00:42:16] process everything because it's a condensation of so many things that are happening. I would like to ask you some practical question, and this is what I post in the chat about, you know that The UN is now changing a lot, the context, international context and financing
[00:42:37] context is changing quickly and then international organizations such as UNFPA or UNICEF, others must deal with this issue of... how to invest, more productively. And particularly in this area of data,
[00:42:53] UNFPA is an organization that, help countries to conduct sensors, to improve vital statistics, et cetera. And most of these institutions are now trying to develop a strategy in relation to data to update.
[00:43:07] their framework because they are from the past. So what complete recommendation you will give to those organization in terms of how what they should include in the data strategy that they ensure that issue of
[00:43:22] governability and issue sovereignty is considered in the framework that you have presented.
[00:43:30] Thank you so much for your question. And I mean, we could talk about this for a long time because linking about or thinking about linking, funding to like what are the priority actions that statistical offices can take and
[00:43:46] especially in the context of sovereignty. I think the first thing that I would say is that there should be a clear mapping and awareness of the capabilities and what are the gaps and understand like where are we sitting when it comes to the
[00:43:58] Mhm.
[00:44:02] the data ecosystem and in terms of like the skills that we have in-house and what are the processes that we are developing in-house and what are the processes that we're maybe doing through third parties. So I think that initial inventory can
[00:44:17] definitely help. make an assessment when it comes to like what are the areas that should be invested in. One, of course, critical area for investment is talent. You want to have in-house talent, but at the same time,
[00:44:29] Mhm. you want to understand like what are the ways through which you can maybe collaborate with other countries, use other countries' tools, or collaborate with external vendors. And that is important to us,
[00:44:42] as long as you know that you are retaining control, especially about sensitive data and you have agency, And the final say when it comes to like what happens to the data when the moment it leaves your office and when it's processed, when it's exchanged.
[00:45:01] So I would start by doing this inventory and understand like what are the gaps in terms of critical infrastructure or skills, the access to compute, access to energy. Then we'll identify what are those,
[00:45:13] if there's investment that can be done, go to some of these critical areas and where there's collaboration possible. And this is not new, just exacerbated by AI. There should be a strong focus about ensuring that data is produced with
[00:45:32] quality, that there is clear metadata, there's clear standards to produce this and that there's also data sharing. So you're not duplicating the work, you're not maybe asking too much of users, but you're doing your data processes in an efficient way. And I think that
[00:45:54] Then I've seen that in practice that.
[00:45:58] development banks and international organizations, they're also starting to look more into like the stage of development when it comes to data and AI processes to, determine funding allocation and funding
[00:46:14] decisions. So Just building these capacities is essential also to.
[00:46:21] have and to have transparency and to have accountability that can help also attract funding for strategic investment.
[00:46:32] Thank you. Thanks, Carla. Then we have a question by or comment by Christian Casanova. Do you want to come in, Christian? Christian?
[00:46:48] Sure. My question is around, because we talked a lot about myself, I'm actually I'm part of the Global Shaper community as well for the forum. But I wanted to ask in relation to the human data supply chain. And so you have a lot of companies like
[00:47:11] Markor, Scale AI, and I can name like another 10 of them that really are in the business of producing human data to train their models and essentially sell data to a lot of the frontier model companies here in the US or also in Europe. So my question to you,
[00:47:32] as we talk about AI governance, compute and privacy, and et cetera. And especially in relation to statistics, where does the layer, where is this human data center layer stand now and where would indeed possibly be regulated as critical AI
[00:47:52] infrastructure? Because these human data is being produced at a scale that may be outside the country at the moment, at least Markor operates that way. But if Europe wants to access that data for any form of statistical significance,
[00:48:07] is that going to be? an issue or is that going to be a regulation as part of some form of statistical production, supply chain or any other form of data that they're using from these human data centers?
[00:48:23] there's a, that's a complex question because there's definitely been a lot of talk about shortage of quality data to feed the models. And there's even been an increased movement towards smaller models and more efficient models just to
[00:48:38] kind of like cut that trend that was going into, let's create more data. And the companies that you're talking about, like Scale AI and such, are definitely playing a key role in this, you know, value chain because of.
[00:48:53] They're sourcing data and sourcing data that is machine readable and AI optimized. So that's another key point. So there is definitely. risks when it comes to how, really fully understanding and having
[00:49:09] full traceability of how this data is collected, because ultimately you don't want to end up with synthetic data that is generated, but that is like so far off from, the ground truth. the populations that is seeking to represent that is,
[00:49:25] no longer usable and can lead, to hallucination or problems in the model outputs. So there should definitely be clear standards and quality checks and assessments when it comes to like, what type of data is being produced in this context for massive ingestion by the
[00:49:45] models and if it's really accurate. And in that sense, I think the role of the National Statistical Office is there any official data providers become even more essential to really, because I mean, these agencies have the closest link to the ground through data,
[00:50:03] to the populations, to the communities, to the businesses, to help, you know, ensure that There's data quality and there's, precision and accuracy when it comes to the data is collected, but... answering to the to the second part of your question.
[00:50:23] There's definitely a need for I wouldn't say stronger regulation, but definitely the standards that ensure that the data that is produced and commercialized follow like all the quality, the key quality criteria, and that there is enough assessment when
[00:50:42] it comes to ensuring that. the data remains through or close to the populations that you seek to reflect and that we consider like moving towards more efficient models versus just moving to having just more data and having more of these models consuming it,
[00:51:06] because ultimately the more optimized models and optimized data sets can lead to high quality results versus just like large volumes of data. So I hope I answer your question.
[00:51:23] Thanks, Carla. Then I have a question, actually. I also put it in the chat. So certain governments control and limit the use of AI infrastructure and AI models for people in other countries due to national security considerations.
[00:51:42] So what can the international community do to avoid a new AI divide which comes then on top of the digital divide that already exists around the world? Maybe you can say a few things about that.
[00:51:58] I mean, there's definitely some amount of data divide. I mean, just between, open source models and non-oper source. And there's different models developed in some parts of the world. And we're seeing that the national security plus the trade restrictions are
[00:52:21] impacting who has access to what model, right? And a lot of times, we know that companies service customers all over the world, but there's we're seeing like in practice a lot more restrictions when it comes to
[00:52:39] access to data or access to these platforms from some countries. So that is happening and there is definitely competition in geopolitical lens embedded into this. But what can international organizations
[00:52:56] do? I think the first thing is definitely help advance alignment when it comes to like the principles that should shape the development of these models, because no matter what model you use, it ultimately, or you would want that it serves the same
[00:53:12] principles, you know, that it's preserves equity, that preserves, a protection, especially for high-risk uses, that preserves, the privacy of the users, that there's like education and support, like building to it,
[00:53:27] especially for vulnerable populations, for kids, as they use these tools. So you will want alignment on those principles. So that's one thing that organizations, international organizations and the global community can do. And there are examples around this.
[00:53:45] I mean, the OECD, the UN are doing great work in these areas, advancing standards. governments like the government of Japan with the Hiroshima AI process, like really advancing standards on how governments and industry can develop.
[00:54:00] these models and adopt them based on shared principles. And the other the other part is really trying to understand the application of the models and try to look into use cases and see where are the specific
[00:54:17] considerations and either positive or negative outcomes depending on the. on the specific use cases and try to develop, if not regulation, at least some level of self-regulation standards that can help ensure that
[00:54:35] ultimately the application of these models also protects humans and that innovation can be done in a way that is secure. They're always going to be that competition and that.
[00:54:52] complex set of providers, but we should ultimately aim that these solutions will have alignment when it comes to like core principles and that its applications will be closely monitored and assessed, especially if they have like a.
[00:55:07] high risk or high negative implications for any of the target population or populations that might be impacted from it.
[00:55:17] Thank you, Carla. Anyone else wants to ask a question? So please raise your hand. That's the easiest. I see Sean, my colleague Sean has a question. Please come in. thanks for the presentation. I just wanted to ask how you see the
[00:55:37] intersection between data sovereignty and the data privacy conversation that typically happens. And by that I mean Most NSOs are familiar with the fundamental notion of privacy. You protect individual responses. And the fundamental tension between
[00:55:57] privacy and statistical offices is that statistical offices are tasked with producing derivative inferences about broader populations, to infer things about broad groups of people that, on the basis of the specific individuals. And we'd like to protect not only
[00:56:15] confidentiality, but through these privacy frameworks. We like to protect the ability of anyone to draw information about individuals against the intention of the data collection. So, if it's a census,
[00:56:29] we use differential privacy to, you know, remove the effect of any one particular person from that data set. Now, in this model, the sovereign is the individual. Maybe I'm misunderstanding this, but essentially because they have,
[00:56:45] we're treating their claim over their personal identity as a fundamental right. Now, I understand that data sovereignty is also looking at a higher level at this issue of, well, upstream to this, you have the fact that if I collect data about a lot of people,
[00:57:02] I can determine population characteristics about people at large. I can put together models about individuals that can and make claims about them that they didn't intend to make. I might infer that you're pregnant because you go into a supermarket at 3 A.
[00:57:16] m. Sort of innocuous behavior becomes very identifiable behavior. And I wonder if this is part of the sovereignty conversation that might be missed. But I just was curious about how you draw the line between privacy and sovereignty.
[00:57:31] Is it that there are legitimate parties that like to make, you know, that have a claim over our data? Is it or do individuals fundamentally reserve all rights to their identity. So just an interesting.
[00:57:45] blow thoughts through my mind. I don't know if you have anything to say about that. Yes, that's a great question. And they're definitely intertwined in any aspects about privacy when it comes to, you know, the signing privacy,
[00:58:02] enhancing or preserving processes and the agency of the subject and, issues related to consent, effective consent as well, and effective right to withdraw, data. I mean, those questions still hold because,
[00:58:17] I mean, ultimately sovereignty means. making sure that data, no matter where it is, it doesn't have to be just in your country, no matter where it is, it will follow the same principles that it will follow in-house.
[00:58:30] So if your country is providing like all these privacy preserving guarantees, just like you said, like to ensure that data can be collected, but individuals cannot be identified and all the statistics can be produced with all the privacy preserving mechanisms.
[00:58:49] All of those principles hold. It's just that You do it, I mean, you're able to maybe host data somewhere else, maybe you're able to exchange data, and ultimately the concept of sovereignty conflict expands those protections beyond.
[00:59:09] maybe makes the privacy conversation even more complex. And because of the context in AI, not necessarily because the context of the sovereignty part of the conversation, just by introducing AI, privacy just becomes a
[00:59:26] a lot more complex because of the complexity sometimes of tracing how like training data has been used and the implications of the outcomes are coming from it. So I would say that they're definitely intertwined and
[00:59:42] And the privacy should remain at the core of, you know, data production processes and you also have different approaches to privacy, right? You have some systems that are more rights based on systems that are maybe more market based in allow.
[00:59:59] for more room towards like what private actors are collecting and doing with the data. So those conversations pre-AI still hold, the GDPR era and before conversations, but now with the sovereignty is about
[01:00:15] like ensuring that all these guarantees hold. no matter where you're hosting the data, no matter where the compute happens, and then ultimately you can ensure the privacy of your citizens or your communities and in the responsible use of data ultimately.
[01:00:34] thank you so much. I really appreciate that response. You're welcome. thank you. Thanks to everyone for all your questions. Unfortunately, we are at time now. But before we all go, maybe you can switch on quickly your camera and your microphones.
[01:00:51] We have a nice tradition here in the global network. Please follow and switch on your camera. Today maybe the crowd is a little bit shy. Well, a few faces pop up. So please join me and give Carla a big
[01:01:09] round of applause. Thank you.
[01:01:13] It's going to mute the noise, I think, with this silent clapping. Okay. Thank you. Yes, I think the noise cancelling kind of Okay. Okay. Anche o collo. filters out the applause. But in any case, you see a lot of happy faces here. Thanks,
[01:01:28] everyone, for joining. Thanks, Carla, for this great presentation, for your thoughts on this and your your inputs. I would like to invite everyone also to join our next two webinars in our AI series. So we have still two to go.
[01:01:48] You can see the registration links in the meeting chat and you have also received them. in the invitation to this webinar today. So thanks, everyone, and have a great rest of your day. Thank you. Bye, everyone.
[01:02:06] Thank you so much. Bye bye. Thank you, bye-bye.