IGF 2025 - Day 2 - Workshop Room 6 - Open Forum #58 Collaborating for Trustworthy AI An OECD Toolkit

>> YOICHI IIDA: Okay. Good afternoon, everyone. Welcome to this Open Forum organized by the OECD. Thank you for joining us here in Lillestrøm and also online.

This session brings together two quantitative discussions. Before I jump into the content, my name is Yoichi Iida. I am the chair of the OECD Committee on Digital Policy. And I'm very happy to be here together with all of you to moderate this session.

So, as first part, we begin with a panel on the OECD AI principles implementation toolkit. That is practical initiative designed to support countries in strengthening their AI ecosystems and adopting governance frameworks to local context. The toolkit will offer region‑specific guidance to help reach AI device and responsive, inclusive AI development.

We will then transition to a second segment forecast on how governments are using AI in practice. To improve public service and policy making. Since 2019, the OECD AI principles have guided national strategies and international cooperation on AI. The OECD principles also serve as a common foundation. Guiding the work of the global national platform GPAI, which recently joined with the OECD community in July 2024, in a new, integrated partnership.

Despite this transformative potential of AI, benefit of this technology remains uneven. Many countries face challenges related to infrastructure, human capacity and the policy frameworks along with greater exposure to risks. Such as task replacement. Today's discussion will spotlight initiatives of those gaps and promote ecosystems around the world.

Please join me in welcoming our four distinguished speakers. Mr. Marlon Ávelos, online, Director of Research, Development and Innovation at the Ministry of Science and Technology from Costa Rica. Second, Miss Lucia Russo, Economist at Artificial Intelligence and Digital Emerging Technologies Division from OECD.

Third -- again, online -- Jibu Elias, Responsible Computing Lead for India from Mozilla. And last, but not least, of course, Miss Anne Rachel Inné, Director General at National Agency for Information Society from Niger. Welcome. We will first hear from the panelists about their experience in designing policies for fostering AI development. After the first round of questions, we'll go around for a short, final reflection from each speaker.

We will then hear from our second segment, which we'll talk about AI in the public sector. Here, we will listen from three distinguished speakers. On my right side, Miss Katarina de Brisis, Deputy Director General at Ministry of Digitalisation and Public Governance from Norway. And also the longstanding representative of policy from the OECD committee.

Dr. Jungwook Kim, Executive Director at Center for International Development from KDI. And Seong Ju Park, Policy Analyst at Innovative Digital and Open Government Division from OECD. We will then open the floor for questions and answer session. To hear from you. And engage in a conversation.

So, we will monitor the online chat. And take questions from the room also. We will be taking questions after the second segment. If you are joining online, feel free to ask your comment. And put your questions in the chat box. If you are here with us in the room, please note your questions down on the note. And we will reply to them after the second segment of this open forum.

So, we start with the first segment. I would like to start with the discussion on collaboration on trustworthy AI. And hear about the design policies and plans for the OECD toolkit. To provide support to countries while elaborating these policies.

I will start with Mr. Ávelos online. Again, Mr. Ávelos, Marlon, from Costa Rica, started the work on the OECD implementation toolkit. So what prompted this initiative? And what has been Costa Rica's experience so far, up until now? In developing national AI strategy from this perspective?

>> MARLON ÁVELOS: Thank you so much for giving me the floor. Good morning and good afternoon, dear colleagues. It is an honor to be in this Internet Governance Forum 2025. I'll tell you a little bit about our experience.

[ Screen froze ]

>> YOICHI IIDA: Seems to have some technical issues online. So we will wait a little bit before we get him back. But eyes, we will proceed to the second speaker. Okay. Thank you for your patience.

Before we get him back online, I would like to proceed to the second speaker. So moving to Lucia. I would like to ask you, can you tell us more about the implementation toolkit with objectives, structure? And how it aims to support government with different levels of AI maturity in policy making?

What is the overall vision for this project going forward? Lucia, the floor is yours.

>> LUCIA RUSSO: Thank you. It's a pleasure being here at the IGF. So as Marlon was starting to say, this project was initiated by Costa Rica. It started off from the consideration that AI opportunities are many‑fold across sectors and across the globe. There are, of course, potential transformative access of AI across sectors. We will hear later about AI in the public sector.

As well as we know in agriculture, in healthcare, in education. And these opportunities, however, are difficult to seize for different countries. As there are several bottlenecks that oftentimes prevent countries from having the capacity or the financial resources, or organization resources to devise effective AI policies.

With these considerations in mind, we started with our delegation, our support of AI. Starting with Japan, Costa Rica, the UK, France, North Korea. To implement the toolkit AI principles. Let me stay a bit on the principles. Which, as we heard, is the foundational document for the OECD in AI governance. And were adopted in 2019.

These principles have, since then, been the object of work from the OECD to provide analytical analysis. As well as guidance on how to implement them. They are constituted by five principles. Which are recommendations to government around areas. Such as research and development, infrastructure, the policy environment, skills and jobs that are required to effectively implement AI across sectors and international cooperation.

They are values that all stakeholders should strive to embed in AI systems. And, of course, to respect democratic values, fairness, transparency, accountability. Among others. So, what this toolkit aims to do is to look at ‑‑ is to provide really practical resources for implementing, facilitating adoption across countries. With a specific focus on emerging and developing economies.

But tailored to the diversity of needs, and available options across countries. So, ultimately, these resources will include more effective and inclusive AI governments. In practice, what this toolkit will look like is an online tool that will be composed of two main elements. The first one being an assessment that countries would need to navigate our policy.

And that would guide them through, on one hand, the areas they would need to strengthen in AI governance. And on the other hand, priorities that they may want to establish. And once this self‑assessment is completed, the toolkit will provide sessions based on best practices in regions that are at the same. Or are comparable and have similar challenges.

So that they can take inspiration from these other countries. The second component will build on the repository of national AI policies that we have on the OECD policy observatory. And we will strengthen by collecting further information on national and regional initiatives.

In terms of the design of this toolkit, one key feature is the co‑creation component. To develop the toolkit, we are currently planning and organizing. And we have already one such regional workshop planned. To have, really, engagement with countries. With the designer of AI policies.

To understand better, on one hand, what are the key challenges they face when devising AI policies. And when thinking about AI governance in their respective countries. And, on the other hand, understand what resources they need. But also, as I mentioned, also understand what practices they have put in place to overcome these challenges.

We will have one such workshop in Taiwan. Again supported by Japan. And we will organize several others. For example, African countries, Central American and South American countries. And we plan to make this tool as helpful as possible.

I think I will stop here in the interest of time. And just checking online if Marlon is there. But I don't see him.

>> MARLON ÁVELOS: I am here.

>> YOICHI IIDA: Okay. Welcome back. I would like to come back to you, Marlon, to explain your experience. So, please.

>> MARLON ÁVELOS: Thank you. This is an immersion experience. I just lost my connection. This is a challenge that developing countries, like us, face in every day, and every time.

I was saying that our decision to promote OECD principles and toolkit wasn't a coincidence. It was an intention based on natural experience. As you can see. We saw a reality while the OECD provided guidance.

Many countries, especially in the Global South, still lack the tools and institutions to turn those principles into action. (?) And necessity, urgency and opportunity. Why necessity? Because the IR revolution is reaching our culture. But the capacity is needed.

Urgency, because we saw how quickly the benefits of AI were concentrated in advanced economies. Leaving others behind. Mainly in infrastructure around AI compute capacity. And opportunity.

We have a chance to move from principles to create policies. Mainly in developing countries. Content, we launched our national AI strategy last October. Currently it's being implemented with the support of over 50 entities. Across environment, academia, society and the private sector.

We learn a lot of these with this process. First, that a successful strategy must be ground in reality. That's why we try to focus on what really matters. Ensuring the ethical, secure and responsible (?). Development and adoption of artificial intelligence always with people at the center. And aligned with our national priorities and values.

We prioritize key sectors, like health, education, agriculture and public services. Reflecting our development goals and comparative advantage. Like environmental, leadership, political stability, and international engagement.

We also incite to build a solid foundation first based on our strategic objective. First, design flexible and adaptive framework. Second is strengthen our R & D ecosystem. Three, develop talent and skill for changing world. And fourth, AI in the public sector for inclusion and efficiency.

Our guiding principle emerged through a diverse benchmark from the OECD recommendations through the process, code of conduct. And through human dignity. As I said, we take the best part of a lot of instruments. For example, we are so inspired by the European Union, the USA risk management.

And AI policies from (?) in America and (?). We don't stop there. We conduct a national rate assessment based on real threats and prior experience. As you can see, we got inspired from a lot of instruments and reference. But one or most important inclusion was international collaboration is essential.

Mainly for developing countries like us. That's why we embed this international leadership as a part of actions in our strategy. Based on our active participation in OECD. As a member in GPAI and initiatives and other programs. Giving us the path to do it.

Designing a strategy like this wasn't easy. Because we had a lot of goals. We had a lot of priorities. But we lack maybe the knowledge that other countries, that the developing countries have.

Even a country like Costa Rica, politically stable and trans‑connected face these challenges. Then surely other countries, like us, will face that challenge. Just a few days ago, a chair of the OECD Costa Rica proposed development of this OECD AI principles implementation toolkit. A tool now endorsed by several countries and members.

Getting to this point required months of preparation and negotiation with developing and under‑developing countries. Thanks to the support and talent of OECD secretariat, represented by Lucia Russo today. (?) building their own AI policies. Self‑assessment and implementation guide that my colleague, Lucia Russo, will explain more in her presentation. Or was explaining after my reconnecting issue.

This is not only a Costa Rica initiative. This is a collective project that is facing cooperation with the support of countries like Japan, Korea, Italy, France, the European Union, Yugoslavia Republic. And other countries that are supporting. Not only politically, but financially in different regions. In central American region, Latin American region and African and Asia.

It will help shape the toolkit's next iterations. Ensuring it adapts as technologies advance and societies change. Lastly, this will depend on two things, we hope. Customization, learning and (?). Processes that will develop over time. And show that AI is actually delivering AI, delivering value for people.

Costa Rica offers its lessons based on our experience in the design of AI policies. In the next tools and instruments that we are designing. For example, in the regulations and other instruments. And ensure our full commitment to help turn the energy that we have that the countries gave us into action. (?) is left behind in the age of this artificial intelligence age that we face in this moment.

I will stop here. And thank you. My apologies for the connection issue. Thank you.

>> YOICHI IIDA: Thank you very much, Marlon, for your sharing the experience and your efforts on this very important initiative. If you allow me to talk a little bit about Japan's experience. We actually started this discussion in the year 2016. And the proposed international discussion to OECD on AI principles.

That was the beginning of the whole process. And when people agreed on AI OECD principles, it is actually very comprehensive and very high level. So, some people said, you know, this is wonderful. But how can we make this into practical policies and actions?

So now we are making efforts together. Not only by Japan. But all together with Costa Rica, Korea, and others. Backed by OECD secretariat. To guide the governments and other stakeholders to make this very comprehensive set of principles into actions and practical policies.

So, this is a wonderful process. And I'm very happy to hear these two presentations. Now I would like to move on to Jibu Elias from Mozilla, online. So based on your experience and work with Mozilla, and also your experience in India's ecosystem, Jibu, what types of community‑led or policy‑driven initiatives have proven most effective in supporting responsible AI adoption?

Particularly in emerging economies? What insights can we derive from these initiatives that could be relevant for policymakers? Jibu, the floor is yours.

>> JIBU ELIAS: Thank you very much. It's my pleasure to be here to share our experience in India, one of the most advancement tech environments in the world. Let's begin with the foundational truth. In emerging economies, AI is not just a question of capacity. But a larger question of context as well.

Responsible AI must be inclusive, accessible, and rooted in the values and live realities of people it should serve. At Mozilla Foundation, we strive to meet these challenges head on. Through a unique initiative called Responsible Computing Challenge, or RCC. India has the world's largest ‑‑ or second largest developer of operations in the world. Yet there are a lot of shortcomings.

For example, ethics, accessibility, and inclusion are almost entirely missing from the mainstream AI or even tech. The workforce in India is concentrated in urban clusters, around Bangalore or (?). Leaving the smaller cities, tier two, tier three, rural communities and especially female workforce women behind. People are rightfully skeptical of systems that affect their jobs, access to welfare or even their freedom.

In RCC India, we decided not to start with rather abstract frameworks. We focused with people. Especially students, academic faculties, women community. Marginalized groups like tribal organization. And first‑generation learners, who have never been asked what responsible AI meant in their world.

So, from a starting point, we decide a deeply localized and community‑rooted approach. Where we begin with this question. What does responsible AI mean to those who are most affected by it? But at the same time least responsible in building it. Our answer came from the communities we mentioned before.

Students, marginalized communities and, importantly, young innovators around the country. One of the most striking experiences came from one of the colleges we work with called Marian College in the western area campus. They became desperate for some of our ethical tech innovations. One is a standout AI‑powered tool called web base. Which was developed by a first‑year BCS student.

So open source, AI‑powered accessibility region. Which builds redeveloped by the university. It's used by 30 websites across the world. And even received a design patent from the India patent office. It's a project that even first‑year undergraduates were empowered with ethical frameworks and open tools can create global public goods.

Similarly, we had Physio Play, which is an AI‑based software simulation to help physical therapy students. Designed to help them build diagnostic skills through game play based on real‑world case work. Built by a physiology student.

SpeedWords, a communication coaching platform. That provides AI‑powered feedback on fluency, filler words, grammar. Supporting students to prepare for interviews and presentation.

Fin Stage, which was developed by a community of students from Maharashtra. They are coming from the marginalized groups who do not have the privilege to access to high‑tech technology or access. It's a personal finance chat board that teaches college students about budgeting, saving, financial planning in (?) sessions.

Each of these tools we mentioned here are possible community‑based, community‑rooted tools. In some cases built by students by their peers. Understanding what is lacking in their ecosystem. What they need to build. Their ethics, responsible pillars are focused on AI. And open source first.

They represent not just innovation. But how does democratized leadership looks like. Students led demonstrations ‑‑ what responsible tech looks like ground up, when you work with faculties that lack initiative and address another critical frontier of AI. Such as scalability in high‑stake governments.

So our work with information technology (?) they developed something called the practice lab. Which launched explainability dashboards, designed to tackle the larger (?) problem in AI. It helps users understand why an AI system using shared values (?) and metrics.

Similarly, we developed data from AI. Which enables real‑time interactive testing of AI predictions on real datasets. Making model behavior to even nontechnical uses. And finally IXI, which applies explainability to medical AI. By using heat maps to highlight what influence diagnostic physicians in (?)

These are open. The key impact is that they give everyday users, and regulators, and policymakers the ability to question and, importantly, correct the course of AI. This is the future of public AI infrastructure. Transparent, participatory and grounded in accountability.

Finally, our most powerful insights came not from labs. But communities often left out of the AI conversation all together. So after developing technology (?) we ran ideas on the students from rural, urban backgrounds. Where we guided activities in the inquiring stages of (?) and identified challenges in their own communities. From risk management to safety to (?).

(?) applying in a more practical and personal way. We took our system even further to an area called (?). It's a tribal area in eastern (?). Where we conducted workshops with 56 tribal women. Many who have never accessed AI tools before.

We did a local language (?) through storytelling, pictures and use of AI tools, such as ChatGPT, and mapped real problems. Such as unemployment, safety, healthcare. And explored how AI could support micro‑enterprises in herbal medicine for production, and arts and crafts. Which are the prominent employment that these people use.

These are not just minimal tech exposure. But rather hybrid tech transformation. Powerful tech like AI on cultural grounding, peer collaboration and first‑generation design.

These prove that responsible AI doesn't begin with the tools. It begins with trust. So by wrapping up, the main lessons from India AI ecosystem and what we see works in Global South or global management as we call it. Especially having worked in the intersection of civil society, academia and national policy.

We need ecosystems that are locally rooted, capacity driven and, above all, people‑centered. And the most powerful lesson here is that don't just ask who builds AI. Ask whose future is it building? In countries like us, trust is not a given. It's earned.

When communities are trusted as cocreators, not just end users, they don't just adopt technology. They transform it. So, if you want AI that is safe, just truly inclusive, we must design. Not only the code and policy, but the humility, memory, and imagination as well.

Thank you very much for this opportunity. I will stop here.

>> YOICHI IIDA: Thank you very much, Jibu, for this wonderful story. And it's great to hear about these experiences from the ground. Congratulations on your work. India's success with AI and public goods. It's a good example of good policy practice. I'm very happy to hear that the responsible AI principles are backing up such successful innovations.

So now I would like to turn to Miss Anne Rachel Inné. So from your perspective as a digital policy leader in Africa, what are some of the key opportunities and also challenges for African countries in developing inclusive and context‑sensitive AI policies? How can international initiatives drive through the OECD AI policy toolkit better support countries in that region? What key considerations should be made? So Anne Rachel, the floor is yours.

>> ANNE RACHEL INNÉ: Thank you very much. And good afternoon, everybody. I'm actually very happy to go after Jibu in this conversation. He gave a lot of examples that I can relate to. I will start by saying that in the global AI index, it places African countries, in general, among waking up nascent when it comes to AI investment, innovation, implementation, in general.

So, for example, Egypt, Nigeria, Kenya are nascent. While rural coast South Africa and Tunisia are waking up. There's a lot more waking up. I really hope we will soon all be graduating. We do face opportunities and challenges. And that is in developing, as Jibu said, included context‑sensitive AI policies.

I'm pretty sure international initiatives, like the AI OECD toolkit, can help. It can give us a few places where we can pick and choose. And also make sure that we look into others' experiences. So that in doing what we have to do to get there, we do it the right way.

So in terms of the key opportunities, for example, we do have development barriers that can be alleviated. AI can accelerate our critical sectors, like healthcare. In there, for example, if I take the case of my own country, that is Niger, we started years ago a program on smart villages. And we started with healthcare.

So, telemedicine that is geared mostly to skin diseases. Because it was easier to take pictures and send to dermatologists. And get treatment to people. And also, you know, disease prediction.

But it's gone to the point that, for example, I have a group of young people right now at home who are looking at ‑‑ who are working on a device. Remember the oximeter during COVID? Where you would measure oxygen levels in a person that was sick. A lot of researchers found out that, for example, that is a device that does not gauge oxygen level the right way in people who are melanated.

It was something they decided to do during COVID. Today they have a little device that is like the regular oximeter. But whereby the light can penetrate a darker skin. And give true measures of what oxygen level is in a person's body.

And in agriculture, precision farming, agri‑forestry is where we're using AI. Education, of course. Personalized learning. And use of languages in general. This is a place where nobody grows up with just one language in Africa. Hardly any.

It is important that when we're trying to get context AI that we make sure that, to get trustworthiness, we have people who understand what's really in it for them. We tend to have policies that are geared to people who can read and write what we call the official languages. And then we forget in our settings that we have about 60 to 80% of our populations that are still rural.

They don't speak English. They don't speak French. If you want them to be part of this, you really have to explain it to them in your language. And that's also one of the reasons why the little applications that the kids are doing in terms of software recognition that can help people, whether in Fintech or healthcare and others are really helping.

We do have another opportunity. Which is simply we have a very young population in the region. Now, we do need a skilled workforce. So capacity development and deployment is something that we absolutely need.

Now, one of the big constraints that also comes with that is kids do not grow at the speed at which artificial intelligence is growing. I want to take, again, my own country. We have 65‑plus that are under 25. And 60% under 15.

It's a very young population. As much as we need capacity building, we need to give it time. For the kids to get to the point where we can have a sound and real workforce.

We do have ecosystems that are really growing AI solutions. That are geared to the local place. As in, for example, using a lot of mobile financial tools. To make sure from women agricultures. All the way to land sharing and deeds recognition in rural areas.

Things like that are being done. So, those are, you know, some of the key opportunities. And, of course, we do have the regular challenges that everybody knows in terms of infrastructure. Again, when I take the case of my country in the African region, we have 16 countries that are landlocked.

So connectivity infrastructure is already something that is quite dear. We do have issues with electricity. We still have ‑‑ you couple that together. And you have only about 22% of Africans that have broadband access.

So, that's still something that we need to work on. Because it exacerbates the divide. In terms of policy and regulatory frameworks, we have a de‑fragmentation also. Because many countries lack cohesive strategies, AI strategies. Or harmonized regulations.

You do have uneven implementation. Or even missed cross‑border collaboration opportunities. Because we don't ‑‑ in as much as we have some of the ministerial meetings on the continent to talk about one policy or the other, we absolutely need ‑‑ if we're going to use AI tools in Fintech, we need to know that it's not just the minister talking about this.

We need to make sure if we're going for national I.D. that the person who is going to be I.D.'d understands the reasons why. And what it's bringing to them in terms of advantages. And we also need all the different government ministries, like interior, defense. All the way to the national data protection agency. To talk together. To make sure what is put in place is really protecting people's privacy.

So also have, of course, (?) in bias. As I said, we have a lot of official recognition systems globally that are trained on non‑African data. And they perform poorly on our people. In general, as we're speaking, about 2% of data generated on the continent is used locally.

So it's basically hard to get real data back to our institutions. Just because it's managed by global platforms that do not necessarily want to share it readily with us. And, again, we do have the capacity constraint. Because the government struggles to keep pace also with AI advancement.

You basically started talking about data privacy. Your agricultural minister wants to put a lot more stuff in their environment and everything. We've come to the point where governments are having a hard time sifting through their data that they have locally.

So toolkits like the OECD one can help. But it can only help if we really have modular guidance also. So things like Jibu and Marlon talked about are really interesting. And can be looked at. That can also help some of our countries.

It's much better to have real‑case users than generic benchmarks. Those are great. But they don't really show you how to make it work at home. So, in terms of capacity building, we need definitely more AI research centers.

We need policy training. And sharing with platforms. How to make that happen is also one of the things that we're grappling with. And we need all of that, of course, so our own policymakers can be empowered to have discussions. At the level where then policies can trickle down to people.

Of course, we all talked about it. Inclusive governance. We must include ‑‑ globally, we must include African voices, to avoid the one‑size‑fits‑all. I love the idea of that oximeter. We all saw it and experienced it somehow.

To suddenly discover that this little device that we were trusting to do something is not really doing the right thing for us was really eye opening. It's important that everybody's perspective is taken into making sure that these global toolkits are done the right way. Looking at people's, I guess, particular settings and context. So in terms of developing private/public partnerships.

It is something that's starting to get traction more in the region. Because, of course, government cannot do it all. We absolutely need the private sectors to, you know, be part of this whole process and tool. To make sure they can develop things that they can lead on.

Having said that, I think I will conclude by saying ‑‑ I'm going to say something that makes us all laugh all the time. That maybe a few here can relate to. Unless you're African. We do say Europeans have watches. We have time.

I'm just saying this to plead for, you know, taking the time to do things. Because rushing into doing things that are not geared to the context just keeps us behind. More than anything. Because people do not understand what it is we're trying to do. Or where is it that we're trying to get to.

It is truly important that everybody is listened to. Everybody is part of the discussion. Everybody is brought to the table. So that trustworthiness that we want be not only in AI. But in the whole digital transformation that we want to see in our countries. Thank you.

>> YOICHI IIDA: Thank you very much for this very insightful presentation, Anne Rachel. I saw a lot of commonalities between your country and our country. Like issues such as education. Or spreading the idea is always very difficult in Japan.

But I really agree to the point that, you know, the inclusive stakeholder approach is very important in this discussion. Thank you very much. And for the sake of time, I thank all speakers for their insight and contributions. And now we turn to the second part of our session. Which will focus on how governments are using AI in practice across key public functions.

This is also of relevance to the previous segments, as the OECD AI policy toolkit will have information on sectors, including public sector. I'm pleased to hand over the moderation to Miss Seong Ju Park, Policy Analyst at Innovative Digital and Open Government Division at OECD. To lead the next segment. So, Seong Ju, please.

>> SEONG JU PARK: Thank you, Mr. Moderator. I was recently, back in my Korea ‑‑ my country, Korea. I needed to explain about the history of the palace to friends I had over there. Before I would have searched for the palace. And then tried to understand information I find. And then explain that in English to my friends.

But this time, I just asked ChatGPT to give me a very catchy explanation about this palace. And then I just played it for my friend. So AI has changed many aspects of our lives. How we communicate. How we seek information.

And this is effecting governments as well. This is accelerating digital transformation of public sector. Changing how governments work. How governments design and deliver policies and services. And it also changed the expectations and needs of the citizens and businesses that they serve.

So, before I invite two panelists that I have here, I will quickly present to you some of the OECD findings on AI in government. May I have the slides? Can we put it in presenter mode? Thank you.

AI, as a tool, has a great potential to support government to improve productivity, responsiveness and accountability. So AI can automate and streamline mundane and repetitive tasks into more meaningful tasks. Interacting with citizens and businesses. And AI can also support tailoring processes and government services to meet users' needs.

AI can enhance decision making by supporting governments when making sense of the present. And better forecasting for the future. AI can also support enhancing accountability and detecting anomalies. Also, AI can help governments unlock opportunities for external stakeholders.

So how can governments enjoy this potential benefit in a trustworthy and responsible way? So the work on governing with AI seeks to address this question. Of how to develop and then deploy trustworthy AI in governments. And we started with looking at what has been done across different government functions.

So, we have conducted analysis of use cases across 11 government functions. Covering three broad categories. Policy functions. Key government processes. And service and justice.

So, in total, 200 use cases were selected. And based on the influence, diversity and representativeness. So based on the use cases, literature research and recent policy developments, we were able to identify key trends. Shaping the current state of play. Major risk and implementation challenges that governments face. And also explore potential use and future pathways.

So, the first trend we saw is the use cases are unevenly distributed. There are a number of potential explanations for this distribution that you see on the screen. I won't be able to share all. But I will try to share a couple with you.

So the policy functions most represented tend to be the ones most in the public eye. Potentially suggesting a focus on areas that have immediate visibility to citizens. Factors going into this could involve both demand from the citizens. But also a desire among governments and political leaders to visibly demonstrate value of using AI in government.

And we also found that some functions face particular barriers or complexities. Such as particular stricter rules on data access and sharing. And stricter requirements for thorough (?) trails in public integrity.

Another trend we saw is a big emphasis on automating and personalizing processes and services. This is slightly more than half of the examined use cases. They seek to contribute to the automation, streamlining and personalization of government processes and services. Particularly in justice, public services, civic participation, and regulatory design and delivery.

We found that four out of ten use cases seek to (?) decision making, (?) making, and forecasting. With most concentrated in public services, regulation and civic participation. I have some of the use cases. I won't be able to go through them.

But the OECD is planning to launch a more comprehensive report. Where you will be able to find all 200 ‑‑ some of the 200 use cases that I mentioned earlier. So, I will skip through different use cases we found for supporting different functions of the government. And I will go to the most important topic when it comes to government ‑‑ AI in government.

So it might not be a fun topic for us to discuss. But government's use of AI comes with higher risk. It has potential dangers and threats that could seriously harm individuals' lives and also society as a whole. It could potentially undermine public's trust in government.

The ministry of government's AI use. And even democratic values. So, to address these concerns, it is important to continuously consider potential risks that may not exist today. And here on the screen, you see the general five risks we identified throughout our research.

These range from ethical risks, operational risks, exclusion risks. To public resistance. And missed opportunities. And a widened gap between the public sector and then private sector capacities.

So beyond grappling with these risks, we also found that governments all face a number of implementation challenges. When seeking to develop and use AI. So we found that there are many use cases. However, they remain at a piloting stage. And many are struggling to scale the pilots into the wider systems for services.

And also there is a large room for improvement when it comes to actionable guidance. Also governments need to navigate rigid regulatory environments. And the next challenge is shared by almost every government on this planet. There are inadequate data, skills and infrastructure in the public sector.

In addition, governments need to better understand the cost and benefit of AI in the public sector. Many are still ‑‑ the cost and benefits around the use of AI in government is quite unknown. That makes it quite difficult for policymakers to make business cases to scale up their AI efforts.

So to support governments to mitigate this risk and overcome these challenges, we have worked together with the OECD and partner countries on a framework to support government's AI efforts. This is an evolving framework. We only seek to provide guidance for countries. So they can continue on through this AI journey.

As you can see, the framework is organized around three sections. First is the level of engagement. This includes the different stakeholders that need to be engaged in the responsible use of AI in the public sector. Our previous speakers mentioned involving different stakeholders.

Not only from the public sector. But also from private, academia users into devising AI strategies or developing AI solutions. It's important to have different actors around the table.

Then the second element is enablers. So, enablers include areas where policy actions can be prioritized to establish a solid enabling environment. And then unlock the full‑scale adoption of AI in the public sector. So these areas include governance, capabilities, collaborations, and partnerships. Where policymakers currently indicate the existence of important constraints and shortcomings.

The last element is on guardrails. So, guardrails include options for policy levers that governments can consider developing for a responsible, trustworthy and human‑centered use of AI in the public sector. This can range from soft laws and guidance or standard to legislation on AI. Enforcement mechanisms. Or oversight bodies.

So this work is part of a bigger OECD project called Horizontal Project on Thriving with AI. Under this project, there are specific deliverables focusing on delivering AI in government. As I mentioned before, there will be an OECD report on governing with AI. Which goes much deeper and into details of what I just quickly presented with you.

And then there will be a dedicated hub for AI in the public sector. It will be on OECD.AI. It will be sort of a repository for policymakers, practitioners and researchers. And we are planning to have a global data collection exercise on AI policies and use cases. Which will also be presented through the observatory.

So thank you very much. That was my very quick presentation on ‑‑ just to give you an idea of where OECD research has been when it comes to AI in government. Now I would like to invite two panelists. To hear from them on what it means for governments to harness AI in practice.

So, the first topic will be around AI opportunities in the public sector. I would like to invite Katarina first. Katarina, Norway has been exploring AI through the efficiency and effectiveness of public sector services. Can you share with us some early impact that you see? Or early impact that you expect from Norway's AI use in government?

>> KATARINA de BRISIS: Thank you for your introduction. Artificial intelligence tends to be perceived, by now, as being ChatGPT. But artificial intelligence is much more than that. It has been applied in using Norway already in some years. Especially in the health sector, we have several applications that are having a practical impact on people's lives.

One case is our (?) and hospital community. Where they implemented AI, analyzing X‑rays of fractures. And it really saved time for the patients. By 79 days. Many patients, about 2,000, were able to go home immediately. Instead of waiting for results of their analysis and their diagnosis.

And this is now being deployed to several other hospitals. So, it gives really practical benefits on the ground. Then we have our Norwegian tax administration that has used AI, developed an AI model. Which, combined with the rule‑based models, analyzed deposits of tax returns. Looking for missing returns on lending out secondary homes.

That actually led to 85% detection rate across ‑‑ opposite of 12% before. And it saved taxpayers $410 million. That was the additional revenue they were able to produce. There are hospitals using AI to produce three‑dimension maps of internal organs to have more direct treatment. It's already been in use since 2023.

There are also hospitals using AI to give more accurate analysis of patients with epilepsy. That can diagnosis it precisely and quickly. Our state launched student agency uses AI to control housing. They use housing verification checks.

Just to be sure that no public funds are misappropriated by students. Saying we are living there. While they are actually living some other place. And collecting grants for that.

Our police authorities use AI for transcriptions of interrogations. When they do investigation on a crime. Which saves a lot of time. Because the AI just transcripts spoken language into written language immediately.

So, in general, we have a lot of these kind of use already. But still the potential is very great. We have done a state employer survey in 2025. Which asked 200 state agencies about their use of AI.

70% answered, do they actually use AI in their daily work? I think this is mostly generative AI assistance. Which they use for things like designing job advertisements. Case processing. Analytical work. Helping them in recruitment procedures and this kind of stuff.

But this is state. We have about 400 or more municipalities, which are very small. And potential there is much greater. We still have a way to go there. And what we also need to work on is better tools to assess benefits from AI.

We have cases. We have real benefits already produced. But to look across the board. And have some tools that will really give us methodology background to assess benefits of introducing AI in various sectors and government levels. That, we need to work more on.

So, I'll just maybe finish here.

>> SEONG JU PARK: That is a very important point. Many governments are still trying to find out the best way to measure what benefits and the impact use of AI in the long run. In some cases that you share, it clearly demonstrated that use of AI has supported the Norwegian government in its efficiency. But also in enhancing people's lives. Saving them time and money.

Then I will go to Dr. Kim. Dr. Kim, you have conducted extensive research on digital technology, including AI. And for enhancing services and policies. Can you describe the key elements that governments should consider when using AI to ensure that it is used effectively, innovatively and inclusively?

>> JUNGWOOK KIM: Thank you. Korea is like one of the leading countries in OECD government index. Which was published recently. And research states.

There's different stages of development or adoption of the AI technologies in public side. But I am pretty sure that there is no communication. It's a long journey. The government services delivered to the public. So I would like to explain and address some of the key enablers or pillars of the Korean history of AI adoption or digitalization in public services.

The first one innovation. Innovation is change. Change in your life. Change in what you work. And change in what you address your needs and how you develop your services.

So, for the innovations, we have some different aspects of the targets. One is data. So we need open data. But we need a much readable data. Which is not available before. Which means we need to make some research or development in data.

Assessing data. Processing data. And changing the data formats. So that we can utilize it in AI adoption. So we need change in the data.

And the other one is infrastructure. So, each and every government has infrastructure in daily need. And providing public services. But for the adoption of AI has challenging aspect. That means we need innovative AI to take care of the current infrastructure of the public service delivery.

And the third one is public service delivery itself. That means we need to bring citizen censored AI public services that was not available before. However, it is visible. And we need to point out the way we provide the services. And the way we try to address the demand by public citizens.

Those are innovations, data, infrastructure and public service development. And the other pillar is inclusion. We should take care of the digital divide for sure. And we experience digital divide. Even Korea experiences digital divide. By gender, by region, by income or by education.

So, we need to enhance accessibility for the AI adoption for public services, of course. That might be enhancing accessibility through AI. Delivering hyper‑personalized services by the public sector. Or focus on the effectiveness success of isolated groups. So they can assess easily for the public services.

The other one is capability. So we need to educate. We need to train the public officers as well as the citizens. It's changing the life in a way to take care of the issues. So we need inclusion. Which can be separated into, like accessibility (?) education for product building, and capability increase‑ment or accessibility increase. So those are the two pillars of the AI (?) in public policies.

And the final element is investment. That requires resources to adopt, develop and deploy those AI services into the public sector. So, innovation and inclusion requires investment. So you should spend your money wisely in order for the AI adoptions.

>> SEONG JU PARK: Thank you. The data structure and how we approach public service design. These are the hot topics of many of our delegates as well. And then also the last point on investment. It has put a bigger, more spotlight now on AI.

That governments need to have strategic thinking on how they're going to use public money in investing on public services. I cannot agree with you more that we are on a long journey. And I always say moving target. There's always moving target every day. And no graduation.

I think this is for many governments around the world. So, thank you for sharing the key policy issues. I understand that your work also includes elements to support safe and trustworthy use of AI. How could governments use AI in a responsible and trustworthy way?

What are the key elements to avoid or mitigate the five risks that I mentioned earlier?

>> JUNGWOOK KIM: Thank you. So, the question is dealing with safety or security issues around AI. And it's public work in dealing with technology. And there is big challenges in dealing with those security issues. Especially for the public services. Because a lot of actually detailed, personal data is accumulated and processed in public body.

That means we need to secure that data. That's top priority, that means. So, we need citizens' rights to their personal data. Not just giving access to the personalized data for anyone or some of the stakeholders. Rather you need to build a consensus.

And you get explicit consensus in accessing and personalizing your data for sure. It's a way to secure some of the safety issues in dealing with personalized and privacy issues. The second is security issues. It's vulnerable to hacking or other malicious function of the system. So open infrastructure and wide‑based system has a challenge with that.

System itself should be secured. Should be designed and maintained in a safer way. That is another challenge for dealing with safety issues. The third one is AI safety in governance.

It's a moving target, as you said. We need some measures to take care of the AI safety issues. Some examples which breaches privacy. Citizens' safety issues. There are so many dialogues on those ones. But each and every country should establish those safety and governance in the right manner.

And design a system so they can take care of those issues for a real‑time and in advance. To minimize their risks or uncertainty associated with AI implementation. So, those are not independent from our daily life. Rather, it reflects and has impact on daily life of the citizens on a larger scale.

So, AI employment and deployment should be related clearly in safety AI in governance in each country. This is what we can say based on Korean experience.

>> SEONG JU PARK: Thank you very much. It's important when it comes to data. But also sensitive data. We found that some of the sectors, including Social Security sector, healthcare sector and justice sector, they hold a lot more sensitive and personal information on the users, the citizens and businesses.

And I cannot agree with you more on the need for the agile government. Many governments have been talking about being more agile. But I think we haven't reached the point yet. But it will be important to have governance that would allow proactive and timely measures to prevent or mitigate this risk that we see.

Katarina, I will come to you. What concrete initiatives is Norway implementing to ensure that AI in government are safe and trustworthy?

>> KATARINA de BRISIS: Thank you. Let me start with a couple of reflections on implementing AI. For us, one of the challenges is leadership and competence level. Actually, that will underpin also trustworthiness of AI.

If we have managers in public, state agencies who understand both the opportunities and risks associated with using AI. And we know that 60% of our state organizations already implement measures to increase and produce competence. These are the people that are working managing artificial intelligence‑based systems.

And 43% implemented internal guidelines for using AI. This is building a fundament within each public agency. And it is also a dialogue between the employer, the management, and employee representatives. So also those people have a finger on the levers of how AI is being deployed and implemented in the agency.

And then the second thing is the access to data. I agree with Professor Kim that this is a crucial issue. We have a number of registers. And we have been working for several years on opening that data. The opening must happen in a responsible way.

That's why, in Norway at least, to access personal data for a purpose of training and using AI assistance requires legal basis. So, they cannot just say, okay. I have this data. And I train a system. And here we go. You have to have legal basis.

So, you have to procure this legal basis. That may take time with the legislative branch. When you have that, then you can proceed. But also with the safety and security constraints.

Another thing, of course, is to have a little framework, in general. So Norway is working on implementing the EU AI Act. Which will be the overarching framework for using AI in Norway. We work with EU countries to create level playing field.

Already in 2020, we put forward a national strategy for AI. Which put forward several principles for responsible and trustworthy AI. Those principles are further endorsed by our new digitalization strategy for Norway. Published just recently in the fall of '24.

In that strategy, our government has very ambitious goals. They want public agencies to adopt AI at very quick rate. Already in '25, 80% of public agencies should use AI. And by 2030, 100%.

So, as you see, it's very ambitious. But we work quite diligently to make it possible. Both as in agencies, as I was describing, but also on national level. By investing in infrastructure.

The government has invested, for example, 40 million in developing foundational models in our language. Norwegian and Sami languages. Based on our societal values. So we have systems that really reflect who we are. Not the whole of internet. Sorry.

The other investment we are looking at is our high‑performance computing infrastructure. To enable, actually, develop and train AI at the scale that is needed. So that's also the investment. And this infrastructure may be used by both public and private entities.

For example, we have one startup called Digit Farm. That uses AI to help farmers to predict what to sow, and when, and where, and so on. So requires power. So, this kind of infrastructure may provide even through the small startups and companies.

And, of course, in enforcing the AI Act, we will or are establishing a national enforcement structure. So we will have one national communication authority that will look at the compliance with the AI Act. And we will also establish AI Norway. Which will be an arena for sharing experience, guidance, and testing in a regulatory sand box of systems in a very safe environment before deploying.

And we will also collaborate with our data protection authority on this regulatory sand box. Also systems that are trained on personal data may be tested there. So this is sort of an outline of how we work. Both at the micro level and macro level on how to create trustworthy AI.

>> SEONG JU PARK: Thank you for sharing on what Norway has been doing. I remember this one tool implemented by one of the countries I wouldn't name. It was supposed to support the public sectors with their job. But the users of that tool weren’t really trained on how to use the tool.

At the end, what was supposed to be a supporting tool ended up making wrong decisions for the government. So, I see how building employee capabilities and the leadership around AI and digital is a key to ensuring trustworthy use of AI. So I will conclude our segment here. Thank you very much to you both.

I give the floor back to you, Mr. Moderator.

>> YOICHI IIDA: Thank you very much for the wonderful discussion and segment, too. I apologize to all the speakers in segment one that I cannot come back to you for finalizing comment. But now I will open the floor for audience. For any questions or comment on both segments of this open forum.

So, I'm sorry. The time has run out. So, sorry about the management. But I hope you enjoyed the discussion. If you have any questions, please contact directly to the individual speakers.

Let me also share, we will have another session on AI tomorrow morning at 9:00 on conference room hall. Thank you very much to all the audience. And also to all the speakers. And the session is closed. Thank you very much.