Why science metters in global AI governance

Today’s session begins from a simple but powerful premise. We cannot govern what we do not understand. It is my honor to open this session with a special address by the Secretary General of the United Nations, whose leadership has placed science and multilateral cooperation at the forefront of global AI governance. So please join me in welcoming His Excellency Antonio Guterres.

Thank you very much. There is a computer here. I don’t know to whom it belongs. Excellencies, ladies and gentlemen. Thank you for joining this discussion on the role of science in international AI governance. We are barreling into the unknown. AI innovation is moving at the speed of light, outpacing our collective ability to fully understand it, let alone govern it. AI does not stop at borders, and no nation can fully grasp its implications on its own. If we want AI to serve humanity, policy cannot be built on guesswork. It cannot be built on hype or disinformation. We need facts we can trust and share across countries and across sectors. Less noise, more knowledge. That is why the United Nations is building a practical architecture that puts science at the center of international cooperation on AI.

Thank you for watching. and it starts with the Independent International Scientific Panel on Artificial Intelligence. This panel is designed to help close the AI knowledge gap and assess the real impacts of AI across economies and societies so countries at every level of AI capacity can act with the same clarity. It is fully independent, it is globally diverse, and it is multidisciplinary because AI touches every area of every society. And I’m delighted that the General Assembly of the United Nations confirmed the 40 experts I proposed to member states. Now the real work begins on a fast track to deliver a first report ahead of the Global Summit. The Global Dialogue on AI Governance in July. The panel will provide a shared baseline of analysis.

helping member states move from philosophical debates to technical coordination, and anchor choices in evidence so policy is neither a blunt instrument that stifles progress nor a bystander to harm. That is how science transcends decision -making. When we understand what systems can do and what they cannot, we can move from rough measures to smarter, risk -based guardrails. Guardrails that protect people, uphold human rights, and preserve human agency. Guardrails that build confidence and give business clarity so innovation can move faster in the right direction. Science -led governance. Governance is not a brake on progress. It is an accelerator for solutions. A way to make progress safer, fairer, and more widely shared. It helps us identify where AI can do the most good the fastest.

And it helps us anticipate impacts early, from risks for children, to labor markets, to manipulation at scale. So countries can prepare, protect, and invest in people. Today, international cooperation is difficult. Trust is strained, and technological rivalry is growing. Without a common baseline, fragmentation wins, with different regions and different countries operating under incompatible policies and technical standards. A patchwork of rules will raise costs, weaken safety, and widen divides. Science is a universal language. Guided by the independent panel and the global dialogue on AI governance, we can align with the world. We can align our technical baselines. When we agree on how to test systems and measure risk, we create interoperability. So a start -up in New Delhi can scale globally with confidence because the benchmarks are shared, and safety can travel with the technology.

Finally, let us be clear. Science informs, but humans decide. Our goal is to make human control a technical reality, not a slogan. And that requires meaningful human oversight in every high -stakes decision, injustice, health care, credit. And it requires clear accountability so responsibility is never outsourced to an algorithm. People must understand how decisions are made, challenge them, and get answers. Excellent. Thank you, ladies and gentlemen. The message is simple. Less hype, less fear. More facts and evidence. Guided by science, we can transform AI from a source of uncertainty into a reliable engine for the sustainable development goals. Let us build a future where policy is as smart as the technology it seeks to guide. Thank you.

Thank you, Secretary General, for those inspiring opening remarks. Ladies and gentlemen, we were going to have Mr. Brad Smith. Vice Chair and President of Microsoft Corporation as our next speaker, but he’s running a bit late, so we will move to the next item in the agenda. I would like to welcome Professor Yashwa Bengio to the stage, Scientific Director of MILA and one of the world’s leading AI researchers. He and I will be in a fireside chat and we’re hoping that Mr. Brad Smith will be able to join us very soon. Thank you. So, welcome Professor Bengio.

Thank you for having me.

Our pleasure. So, you are the most cited computer scientist And I looked it up. You’re actually the most cited living scientist today and have played a unique role at the global science policy interface, including through the UN Scientific Advisory Board and your leadership of the International AI Safety Report. So from your perspective, how do these science policy interfaces actually work in practice and where do they add the most value?

So it’s tricky, right, because there are many different views, especially different interests in business, in different governments. And the role of science, the role of a kind of synthesis of science that we want for the UN panel, that we have seeked for the… AI Safety Report. is to try to make it, to provide a shared understanding as a basis for those political discussions and not be influenced by as much as is humanly possible by those tensions that exist in our societies. And I think it’s particularly important because maybe unlike in the case of climate, the scientists themselves don’t always agree on what to expect for the future or even how to interpret the science that exists.

I just want to add something. So something that’s a little bit subtle about this kind of exercise is that to be able to recognize the uncertainty and the divergences that exist, and where is it that scientists agree, where is it that the evidence is strong, where is it that we have clues that matter. Even if we’re not certain about a particular risk, we might have clues about it. But if the risk has huge severity, in other words, if it does unfold, then it could be catastrophic, then policymakers need to make attention. And it’s always difficult when we don’t have proof that something terrible is going to happen. Maybe a good analogy is tipping points in climate, right?

Because there’s not enough past evidence to be sure that a particular tipping point is going to happen. So the situation is similar in AI in the sense that we don’t have the experience of, say, machines that are really smart and can change society, and be even potentially smarter than us. So how can we deal with the right policy decisions? but that’s why it is so important to have as neutral and as fact -based evaluation of what is going on available to everyone and in a language that is accessible to everyone and of course for policy makers which by the way is difficult for scientists to achieve they need help, they need iterations they need feedback from people who are used to the interface between science

Is there anything in particular about the highly technical nature of AI and also the pace of change that makes this interface particularly difficult?

Yes, yes The facts shown in the scientific benchmarks across labs, companies and academia on AI show very rapid growth of the capabilities of these systems and the capabilities of AI and the capabilities of AI and the capabilities of AI and the capabilities of AI and the capabilities of AI and that growth is uneven so we see AIs even surpassing most people on some measurements of capability and being kind of stupid or like a six -year -old on some other things so it’s very difficult to grasp what that means but because it’s moving so fast there’s always going to be a lag between even like the scientific papers take time to be written if there are studies so think about studies that involve people they’re going to take months and so by the time we start seeing clues that there’s a potential problem so you can think of something recent that was not expected like the psychological effects on people of these chatbots we now have lots of anecdotal evidence and we’re only starting to see the scientific studies and of course on the policy side it’s going to be even more difficult even later because those discussions are going to happen after we see scientific evidence so there is going to be a lag and that’s a real problem because things could move

So maybe that leads well into our next question. We often hear that AI governance is moving too slowly and from your experience, what kinds of scientific assessments or benchmarks could realistically keep pace with this rapid change?

Yeah, that’s a great question. My opinion on this is that we should be thinking about not just policy and the usual sense of coming up with principles, but we should try to strive for high -level principles that can be applied without having to go into the details because the details are going to change. And the second thing is I think we should strive for technology that are going to help to implement those guardrails in the field, in the deployment of AI, because otherwise there’s not enough time to

Well, thank you for those insights. And also congratulations on your recent appointment to the Independent International Scientific Panel on AI. In a few words, how do you see this new panel helping to strengthen the link between science and global AI policymaking?

Well, I think there’s something really important about this panel, and it’s global aspect and being rooted in the UN. And the reason I’m saying this is that AI is going to be transforming our world very clearly, and it’s going to have global effects, whether it is on the good side, the benefits are on the risks, but also the kind of power relationships that are going to be changing in the future. And I’m personally very concerned about how this will unfold for developing countries in the global south. And we need to work. Thank you. in a multidisciplinary array so that we can foresee those effects and we can start discussions to make sure that everyone is at the table and no one is on the menu.

Well said, Professor Bengio. Well, thank you very much for kick -starting our discussion. We will now turn to our panel. So, ladies and gentlemen, it is essential that discussions about AI policy include the voices of key industry actors, and I am pleased to invite Mr. Brad Smith, Vice Chair and President, Microsoft Corporation, for his keynote address.

Well, good morning, everyone. It’s a pleasure to be here. My apologies for being a few minutes late. I want to offer a couple of thoughts this morning. The first thing I think we should come together to think about is that, in my opinion, this is a moment in time when we need to reflect on and reinvest in the importance of the United Nations. There is a well -known economic theory that says that humanity is, in many ways, almost destined to repeat its great economic mistakes every 80 years. The reason it’s 80 years is because that is basically the lifespan of human beings. And so every 80 years, almost everyone who had any living memory of a prior financial calamity has left the planet.

If you look at the Great Recession that started in 2008, what you realize is that it happened 79 years after the stock market crash that led to the Great Depression in 1929. And you can follow this series of financial mistakes all the way back to the bursting of the tulip bubble in the Netherlands hundreds of years ago. I think there is a corollary worth thinking about. Just as there is a risk that humanity forgets the mistakes it made 80 years ago, humanity runs the risk of forgetting the great successes. it created 80 years ago. It was just over 80 years ago that the world came together to create the United Nations. It was, in my opinion, one of humanity’s greatest accomplishments of the 20th century.

It is a unique organization in a very imperfect world. And so, of course, on any day and any year, it is possible for anyone to blame the United Nations for the imperfections that we see all around us. But the truth is this. Those imperfections are fewer, and their consequences are less disastrous, in my view, because of the United Nations. And one of the great things about working at Microsoft in a job like Microsoft, in my opinion, is that I get to work in a global organization. We have subsidiaries in 120 countries. We do work in 190 countries. We see the world. It turns out that everywhere we go, we see the United Nations. Sometimes it’s the United Nations Development Program, working to foster economic development.

Sometimes it is UNHCR, helping refugees. Sometimes it is the UN Office of Human Rights, seeking to protect human rights. But the truth is, if there’s a problem, the United Nations is almost always part of the solution. We need to remember this. And we need to remember that however challenging the last 80 years have been, we have managed, as humanity, as a species, to live. with the ever -constant presence of nuclear weapons without using them or destroying ourselves. The United Nations has, in fact, in my view, been indispensable to not just the protection of people, but the preservation of our species. Why does that matter now? Why should we talk about it today and this week in Delhi?

Well, because here we are on the cusp of the future. A technology that we all know will likely change the future. Here we are in the second month of the second quarter of the 21st century, and we need to focus on how we bring the institutions on which we rely into that future. So then let me talk about a second aspect that I think is so important to think about this month. One of the things I’m constantly struck by… leading a global organization is how often everyone disagrees with each other about almost everything. But one of the things I’ve learned along the way is that I think one of the reasons people so quickly disagree is that we rush so quickly to debate competing solutions.

This happens in domestic politics. It happens in international diplomacy. It, frankly, happens in a global company. It actually happens everywhere, even in families. As soon as there’s a problem, people want to talk about the solution. And then people have different solutions, and then they debate, and they disagree, and they argue, and sometimes it’s even worse than that. One of the things I’ve learned is the reason people so often disagree about the solution is they don’t have a common understanding of the problem. They don’t spend enough time talking about the problem. They don’t have a shared contextual understanding. of the problem they’re trying to solve. They’re too quick to want to blame someone for the problem, and then that spirals into a discussion that becomes completely unconstructive.

Why does that matter today? Because what we’re here to talk about today is all about creating a more common understanding together based on science of where artificial intelligence is going. This is an indispensable tool. Indeed, it’s a critical service for humanity so we can all learn together, we can all think together, we can all understand together what is going on in the world. I think it’s especially critical, to be honest, when it comes to artificial intelligence because I think if you even communicate, consider most of the conversations you have about this technology. I would argue that it has two flaws. The first flaw is it usually involves people making very grandiose predictions about the future.

You know what? I’ve worked in the tech sector for 32 years. I have listened for more than three decades to my colleagues in my industry around the world make bold predictions about the future. No one ever holds them accountable a decade later for whether they were right or wrong. I used the researcher agent in Microsoft Copilot a couple weekends ago, and I loaded a lot of names. I won’t say whom, but you can guess. And I said, look at all the predictions they made about all the technologies, and look at the predictions they made about when these technologies would come to do something or another, and give them a grade. The average grade was 25%. You couldn’t even get close to the top.

You were at the bottom. So let’s just understand one thing together. There is no such thing as a crystal ball. No one has one. But what we do have is the ability to understand where we are today. And what we do have is a better understanding to just appreciate what is happening each and every year. There is a second flaw, in my view, in many of the conversations that take place, including at this AI summit. Everybody wants to talk about how they’re going to make machines smarter. That’s interesting. I think it’s interesting to imagine living in a world where a data center is like a country of geniuses. But as I mentioned yesterday, compared to the people who lived in the Bronze Age, we’re all geniuses.

We’re all geniuses already. What that should remind us… is that human capability is neither fixed nor finite. And so what really matters, in my opinion, is not whether we are going to build machines that are smarter than humans. Yes, in some ways we will. But how will we use those machines to make people smarter, to help us do what we need to do? That is what this effort is all about. Wow. Let’s harness the power of science to build a common understanding of what is changing each year, and then let’s connect it with the global dialogue on governance so we can pursue policies that will ensure that this technology serves people. There’s no better place to get started than here.

There’s no better time than now. And let’s face it, there is no better institution on the planet that can do more to serve humanity and protect the world. than the United Nations. And on behalf of Microsoft, I just want you to know we are putting our full energy and resources to do everything that we can to help. Thank you very much.

Thank you. Thank you, Mr. Smith, for those insights on responsibility, accountability, and the role of industry. We now turn to our panel. Our panel brings together scientific leadership, public policy expertise, and international coordination. Please welcome to the stage our speakers, Professor Balaraman Ravindran, IIT Madras, Swaminathan, former Chief Scientist, WHO, Ajay Kumar Sood, Principal Scientific Advisor to the Government of India, and Anne Bouveraud, France’s Special Envoy for AI. I am also pleased to introduce our moderator, Amandeep Singh Gill, Undersecretary General and Special Envoy for Digital and Emerging Technologies. I invite him to guide the discussion. Thank you very much.

Thank you very much. Thank you, Anil, for leading us and for those who have not read his book, The Elegant Math Behind Machine Learning, please do have a go at it. We cannot govern something that is not possible. Something that we don’t understand. So something as simple as, like, if 90 % of AI is matrix multiplication, a 0 .01 % as he was explaining, improvement in efficiency of matrix multiplication has huge energy implications. So I want to welcome our esteemed panelists. The stage has been set by very inspiring keynotes and a fireside chat. So we will dive straight in. And since we are running a little short of time, I’m going to compress the two rounds into one rapid -fire round.

So all of you have worked on or are working on the science policy interface. And my sense is that there is a loop here, that there is a loop between science and evidence, and evidence and… and policy. And we want to explore that loop today in the context of the significant development of the setting up of the International Independent Scientific Panel at the United Nations. So I want to start with you, Soumya. You were the first chief scientist, first woman chief scientist at the WHO and worked at a very difficult time during the COVID when evidence, trusted evidence was so critical. So in your view, what makes this evidence that comes from science trusted and actionable for policymakers?

The evidence is very rapid. The field is moving so rapidly. In COVID, we had to review a couple of hundred publications every day to understand what was happening on different aspects, on the virus, on the immunology, on how vaccines were working and drugs, and we had to make recommendations based on the best available evidence that day. I think we may be in a similar situation with AI, and it’s wonderful that the UN has now set up this body, which I see as something like the IPCC. I think we do need global governance. We need something like, you know, we’re talking now about preventing future pandemics by sharing data on pathogens, making sure that we have protocols in place where countries are willing to share that data, and also, of course, to share the tools, the vaccines or drugs when they become available, when or in case there is another pandemic.

Similarly, I hope that this scientific body that’s been set up by the UN would also establish systems that would, would link to national bodies and systems, and that would ensure the voices of all are heard. So one of the things during COVID was some of our recommendations were relevant. in high -income countries but not in low -income countries because the context is very different. And the WHO was criticized for this, I think rightfully so, and we need to learn from those mistakes. So it’s the voices, for example, of women, a low -income woman, a farmer in a remote place, is going to use technology very differently from a large farmer with access to lots of machines in Europe or North America.

So if AI has to work for everyone, then we need to make sure that those voices are heard. And ultimately, I think that loop you talked about, sometimes policy is made in advance of evidence. You have to. You can’t wait. But the policy must change. It must ask for the relevant evidence and be able to adapt when that is clear.

Thank you very much, Soumya. I’m going to come to you, Ravi, Professor Balaraman Rabindran. Now, as AI policies begin to take shape and you’ve been involved in some policymaking yourself, what signals from… regulators or public sector users should most urgently guide future AI research priorities? So in a sense, you know, the loop coming back into research.

so thank you for that question so I mean AI right now especially in the global south so we don’t completely understand the implications of adopting AI and how is it going to affect the society, the people livelihood and everything in fact I also feel that we don’t have enough evidence about how AI is even affecting the social fabric how are children getting increasingly isolated with the adoption of AI and whether the effect is uniform between cities and rural India because the cultural setup is very different and so on and so forth so if the government as we heard our honourable prime minister say yesterday should focus more on youth and the impact of AI on youth what is the evidence do we have about what is happening in India so we hear stories about you know how there is dependence of on AI models of children and also people who are mentally challenged and so on and so forth who are under stress but all of these stories are coming to us from the west so what is it that’s happening in India so when we have these kinds of policy decisions that have to be made the government says that AI should be pushing efficiency in agriculture so do we have a benchmark in India that can evaluate the efficiency of effectiveness of these AI models in agriculture what are the kinds of flaws that happens when I for example build a bot that can act as a co -pilot for a farmer so these are bigger challenges so we have a lot of questions

if I can quickly follow up where do you actually see evidence for impact in the sustainable development goals space just a quick example or two

so I I That was not in the notes he gave us earlier, so I have to think on my feet here. So let me take one thing that we are very familiar with, we are working on right now, is on the education space, right? So, for example, we don’t know, we don’t have evidence of AI interventions. How likely is it to change student learning behavior? So we have done some preliminary studies. So the author of the study is somewhere in the audience, because he has been sending me pictures of the stage. So what we have found out is the effectiveness of AI adoption is a direct function of habit. So if the students are using AI more, then they tend to…

But now I don’t know what is the causal factor there. I don’t know if the causal factor is whether they are using AI more, therefore they get better effect, or do they use AI more because they are getting better effect. So these are questions that we have to ask. Even in something as simple as education. I am saying simple because there is a lot of positive buzz around using AI in education. But even there, we need a lot more evidence to come.

Thank you, Ravi, and we’re honored to have you on the new International Independent Scientific Panel. So if I may jump to you, Anne, and you’re an AI scientist yourself. You know, all of us know you as a special envoy of President Macron, who made the February summit happen last year in Paris, but you’re also an AI scientist. So from your perspective, you kind of lived in these two worlds. So what works best for the interface? What kind of scientific evidence would you take to President Macron if you were to convince him to change the policy?

Well, thank you for the question. I studied AI a long time ago, but I’m not really a scientist. But I try to understand, of course. Understanding, I think, is probably the very first thing. And before we move to policymakers, I think it’s for citizens, for us as human beings. The things that we don’t understand… We tend to be more afraid of. I often quote scientist Marie Curie. She wasn’t an AI scientist, but she’s one of the brightest scientists that we’ve had, two times Nobel laureate. And there’s a wonderful quote by her. She says, nothing in life is to be feared. Everything is to be understood. And now is the time to understand more because, of course, there are more things we can be afraid of at the time when she was living and now as well.

So trying to understand things, having scientific panels is definitely the right thing to do. And we’re fully supportive in France of the scientific panel. We’re very proud that Joëlle Barral is our nominee. She’s a scientist in AI and health and a member of the panel. This is absolutely excellent. So, yes, understanding things. is absolutely key. And then maybe just a second point to give an example of how understanding something or not can lead to very different policy decisions in the field of AI and work. We’ve had predictions. I remember in 2013, that was the previous AI revolution, but scientists, I believe, at Oxford said within 10 years, half of the jobs will disappear. We haven’t seen that.

At the AI summit in Bletchley Park, for very good reasons, we had frontier AI leaders in particular, Elon Musk saying within two years, half of the jobs will disappear. So, of course, the fact that this didn’t happen doesn’t mean that there isn’t a risk for work. Of course, there’s a risk for work. But if your potential or probable outcome is the end of jobs, then you need to think about universal basicism. Basic income, what are we going to do with all the people who don’t have jobs? If what economists are saying is that 80 % of the jobs will be transformed, then the policy outcome is training, skilling, reskilling, and helping to educate people. That’s why listening to economists and having the International Labor Organization and other institutions really follow closely what is happening in which countries for younger people, for older people, for women, for men, for different types of jobs, that’s super

Merci beaucoup, Anne. Merci. And I’m going to turn to you, Professor Sood. You occupy an important position within the Indian system, and you look at science broadly. And India has deployed some of these technologies at societal scale. India stack the digital public infrastructure. So how do you look at the AI opportunity, and importantly, how do you look at AI risks? And how are you prioritizing R &D allocations to harness the opportunities, manage the risks?

Thank you very much for having me on the panel. As you know that all the aspects which you asked, we have had very extensive consultations across all stakeholders. And we came out with the National AI Governance Framework, not the regulatory framework, but how do we really handle AI, all aspects. And there we have looked at how do we enable the compute facility, compute resources to our people. Because we are not at the scale when a few trillions of dollars are being invested. So we came out with some framework which we think with public -private partnership we could enable it. And we could see the results of that within a year as demonstrated in AI Summit.

Summit, the release of AI, so on, models and so on. Other aspect which is very important, as you rightly said, the risk assessment. So this is where, as has been mentioned, our experience with the digital public infrastructure, which has been rolled on a very public scale with the safety and security, which is as difficult as in AI. AI, of course, is more difficult. We still do not know the risks. But when we were dealing with the digital public infrastructure, either for the financial transactions or for identity, identity verification and so on, it was a challenge. And that was done by embedding governance through technical design. And this is what we call techno legal, which Honorable Prime Minister said in the Paris summit.

And also mentioned here. So this is where we are suggesting that this could be one way to look at. It’s not that everything is laid out. We will need framework for that. We will need technologies for that. But this is one way which will have a smooth. interaction if we can bring this technological framework.

Thank you so much for those insights. And now that since we are running out of time and I’m going to discriminate against the men on the panel so my apologies in advance. So I’m going to turn back to you Soumya and Aan for like 40 second, 30 second reflection. What do you think in terms of the pace and direction of technology opportunities including for accelerating scientific discovery and risk. What would be your advice for the international independent scientific panel maybe Anne you can go first 40 seconds.

Yes I think AI has a strong potential for helping science we’ve seen that with the two Nobel prizes in physics and chemistry a year back. There’s many more areas in science where AI can help. It can only be possible if we have databases of scientific data that are available to the world and that are constructed by scientists and funded by governments and international institutions around the world. So this is a very important topic for research.

Thank you, Anne. Soumya, you have the last one.

Yes, I agree very much with Anne. And I think that the scientific panel could actually help network many more groups of scientists from around the world, perhaps sectorally, for example, what’s happening in health, what’s happening in education, what’s happening in agriculture, looking at the evidences as they emerge, encouraging research, setting priorities, but also looking at safety and risks, because I think that’s going to be very important. There may be unanticipated risks and harms that we have not considered. And, of course, equity, being a UN -led panel, ensuring that equity is at the heart of AI and it’s being done for public good.

fantastic thank you that’s a great closing ladies and gentlemen please join me in thanking our outstanding panel and we are going to move straight to the closing over to you Anil

thank you to the panel for a rich and forward looking discussion to close this session it is my honor to invite Josephine Teal minister for digital development and information of Singapore to deliver the closing remarks minister Josephine Teal

good morning everyone first allow me to thank the secretary general for his remarks and it serves as a very useful guidance to all of us working in this important technology for the closing this morning I thought that it would perhaps be useful to offer a perspective from a small state Singapore has a population of just 6 million people and more than 30 years ago at the UN we became the convener of the Forum of Small States which still has about 108 members I will just make three points on how we look at developments on this front The first point is that we believe in AI being used as a force for the public good but to do so, it is important that we continue to invest in the science that underpins it and ground trust in evidence This certainly requires sustained investment in research and is also the reason why we set aside a billion dollars in a national AI R &D plan which will include foundational and applied research into responsible AI We believe in it and we have to put money behind this effort There are of course other investments such as in building up a digital trust center.

It’s our designated AI safety institute that has been participating in important conversations on this topic, as well as setting up a center for advanced technologies in online safety. So those are just some of the efforts that we can dedicate resources to doing as a small state. The second point I want to make is that there is almost always going to be a tension between moving quickly, given the pace of AI development, and moving carefully, giving the latest evidence that presents themselves on what we should be paying attention to. Both impulses are necessary, and we believe it is not impossible to try and balance them through integration of science and policy. It is not easy, but it is not an effort that we must give up on.

I should just add that on this score, it will be much better if we can cooperate internationally to develop sound approaches that can also be interoperable across different jurisdictions. And this is one effort that we believe underpins the work that is being carried out by the UN. And this brings me to my third point. I want to highlight the important role that an organisation like the United Nations plays in facilitating global discourse to bridge science and policy. I cannot overemphasise the importance of this effort. We must recognise that global AI governance landscape is becoming increasingly fragmented. There are multiple initiatives, frameworks and institutions. The UN’s unique value lies in your legitimacy and inclusiveness to encourage interoperability across efforts.

The Secretary -General talked about this too. We therefore welcome… We welcome the establishment of the… independent international scientific panel on AI, building on the work of the UN High -Level Advisory Body on AI, which published its report on governing AI for humanity at the end of 2024. We note that the panel’s multidisciplinary approach, covering machine learning, applied AI, social science, ethics, all of these are necessary to address the complexity of AI governance challenges. Finally, I would just like to acknowledge that we now have substantial convergence on the high -level AI principles. Yoshua talked about this. Transparency, accountability, fairness, safety. But the challenge is in operationalizing them. We need to find standardized evaluation methodologies that work across different regulatory contexts.

We need capacity building so that all countries can meaningfully engage with the technical and the technical challenges. We need to work with the technical evidence and not just with the large AI research ecosystems. I would encourage all stakeholders to view scientific input not as a constraint on policy flexibility, but as a constraint on policy flexibility. as a foundation for more durable, effective governance that can maintain public trust. We need to keep the conversations going, one where science informs governance, and governance sharpens science. I would just perhaps end by highlighting Singapore’s continued commitment to contribute to advancing these discussions. We were very fortunate to host the International Scientific Exchange on AI Safety and to bring about the Singapore Consensus on Global AI Safety Research Priorities.

Joshua was in Singapore for this very momentous event. We will continue to participate in joint testing efforts of the International Network for Advanced AI Measurement, Evaluation and Science. We have organized two editions of the Singapore AI Safety Red Teaming Challenge, the first multicultural and multilingual AI safety red teaming exercise focused on the Asia -Pacific region. And as chair of the ASEAN Work Group on AI Governance, we have actively spearheaded efforts to foster a trusted environment in ASEAN by adapting global norms and best practices for ASEAN and in bringing about regional harmonization through the ASEAN Guide on AI Governance and Ethics, as well as expanding it to address the risk in generative AI. We are now working within ASEAN to explore practical tools for AI safety testing and aim to collectively develop a set of AI safety benchmarks that reflect our region’s concerns.

And finally, I’d like to welcome all colleagues to join us in Singapore for the second edition of the International Scientific Exchange, which we expect to take place on the 17th and 18th of May, and we look forward to furthering

Thank you very much once again. Thank you, Mr. Teo, for your closing remarks. This session is now concluded. Thank you very much. Thank you.