How to make AI governance fit for purpose?

Summary

This discussion at the AI for Good conference focused on how different countries approach AI governance, balancing innovation with regulation while maximizing benefits and minimizing risks. The panel featured representatives from the United States, France, China, and Singapore, each presenting their nation’s perspective on AI development and governance strategies.

Jennifer Bachus from the US emphasized the Trump administration’s deregulatory approach, arguing that excessive regulation could stifle AI innovation and harm America’s technological leadership. She stressed the importance of multi-stakeholder processes and warned against over-regulation that might “strangle” transformative AI technologies. Anne Bouverot from France highlighted the Paris AI Summit’s focus on practical actions rather than regulation, announcing significant European investments and initiatives like the sustainable AI coalition to promote AI for public good.

China’s Vice Minister Shan Zhongde discussed their emphasis on open-source development, citing DeepSeek as an example of cost-effective AI innovation, while emphasizing international cooperation and standards development. Singapore’s Chuen Hong Lew advocated for a “light touch” regulatory approach focused on building trust through evidence-based governance, emphasizing the need for practical guidelines and international collaboration.

When asked what keeps them awake at night regarding AI governance, panelists cited concerns about balancing development with safety, ensuring inclusive multi-stakeholder participation, preventing authoritarian misuse of AI, and managing the rapid pace of technological change. All speakers emphasized the importance of international cooperation, talent development, and ensuring AI serves human-centered purposes. The discussion concluded that effective AI governance requires adaptive institutions, strategic investments, and collaborative frameworks that can keep pace with rapidly evolving technology while serving the public good.

Keypoints

## Major Discussion Points:

– **Balancing Innovation and Regulation**: The central tension between fostering AI innovation versus implementing governance frameworks, with speakers emphasizing the need to avoid “over-regulation” that could stifle technological advancement while still ensuring responsible development.

– **International Cooperation and Multi-stakeholder Governance**: The necessity of collaborative approaches involving governments, private sector, researchers, and civil society across different countries with varying regulatory philosophies (US deregulatory approach, European regulatory framework, China’s state-led development, Singapore’s “light touch” model).

– **AI Safety and Security Concerns**: Addressing dual-use risks, including potential misuse by authoritarian regimes for surveillance and military purposes, data security issues, and the need for testing and benchmarking frameworks to ensure AI systems are trustworthy and safe.

– **Economic and Social Impact Management**: Concerns about job displacement, the need for reskilling and upskilling programs, addressing the digital divide, and ensuring AI benefits are distributed equitably across different countries and populations.

– **Rapid Technological Pace and Governance Adaptation**: The challenge of keeping governance structures and policies current with the extremely fast pace of AI development, requiring adaptive and evidence-based approaches rather than rigid regulatory frameworks.

## Overall Purpose:

The discussion aimed to explore how different countries and stakeholders can work together to maximize AI’s benefits while minimizing risks, focusing on practical governance approaches that enable innovation while ensuring responsible development and deployment of AI technologies globally.

## Overall Tone:

The discussion maintained a collaborative and optimistic tone throughout, despite representing different national perspectives and regulatory philosophies. Speakers emphasized partnership and shared goals rather than conflict, with all participants acknowledging both the tremendous opportunities and serious challenges posed by AI. The tone was professional and forward-looking, with speakers showing mutual respect for different approaches while advocating for their respective positions on the innovation-regulation spectrum.

Speakers

– **Gabriela Ramos**: Moderator of the panel discussion, mentioned as running for a position at UNESCO and having worked with UNESCO on the recommendation on the ethics of artificial intelligence

– **Jennifer Bachus**: Acting head of the Bureau of Cybersecurity and Digital Policy in the USA

– **Shan Zhongde**: Vice Minister of Industry and Information Technology and Policy of China

– **Chuen Hong Lew**: Chief Executive Officer of Infocom Media Development Authority of Singapore (note: transcript mentions “India” but context clearly indicates Singapore)

– **Anne Bouverot**: Tech envoy of France, mentioned as having managed the AI Action Summit

**Additional speakers:**

– No additional speakers were identified beyond those in the provided speakers names list.

# AI Governance Panel Discussion at AI for Good Conference

## Executive Summary

This panel discussion at the AI for Good conference examined national approaches to AI governance, featuring senior government officials from the United States, France, China, and Singapore. Moderated by Gabriela Ramos, who is running for a UNESCO position and worked on UNESCO’s AI ethics recommendations, the discussion focused on how different countries balance AI innovation with appropriate governance frameworks.

## Panel Participants

– **Jennifer Bachus** – Acting Head of the Bureau of Cybersecurity and Digital Policy, United States

– **Anne Bouverot** – France’s Tech Envoy, who managed the AI Action Summit

– **Vice Minister Shan Zhongde** – China’s Ministry of Industry and Information Technology

– **Chuen Hong Lew** – Chief Executive Officer, Singapore’s Infocom Media Development Authority

## Key National Positions

### United States Perspective

Jennifer Bachus presented the Trump administration’s deregulatory approach, emphasizing that “over-regulation could kill the transformative AI industry and discourage necessary risk-taking for innovation.” She stressed America’s need to maintain technological leadership through regulatory regimes that foster rather than constrain AI development.

Bachus highlighted security concerns about authoritarian regimes stealing AI technology for “military, intelligence, surveillance, and propaganda purposes,” arguing this represents a significant national security threat requiring protective measures rather than restrictive domestic regulation.

### French Perspective

Anne Bouverot described Europe’s evolution from regulation-focused approaches toward innovation and practical outcomes. She announced major European investments, including 200 billion euros of investment and 400 million euros of commitments, along with initiatives like the sustainable AI coalition.

Bouverot emphasized that the Paris AI Summit focused on “actions and practical outcomes rather than regulation, with emphasis on innovation over regulatory constraints.” She noted AI’s unique characteristic of having “research roots and very quick societal impacts,” necessitating involvement from researchers, engineers, companies, governments, and civil society.

### Chinese Perspective

Vice Minister Shan Zhongde presented China’s balanced approach emphasizing both development and safety. He discussed China’s focus on open-source development, citing DeepSeek as an example of cost-effective AI innovation, while advocating for “people-centred AI that is traceable, reliable and monitorable.”

Shan emphasized international collaboration through the ITU and global standards development, expressing concern about preventing an “intelligence divide” that could increase development gaps between countries. He highlighted various risks including data leakage, model hallucinations, and social structural changes.

### Singapore Perspective

Chuen Hong Lew advocated for a “light touch” regulatory approach focused on building trust through evidence-based governance. He noted that “light touch actually requires extremely heavy lifting” because it involves “building an entire ecosystem” rather than simply creating laws.

Lew emphasized the challenge that “the rate of change outside is greater than the rate of change inside,” requiring deliberate action despite rapid technological progress. He referenced the principle of “festina lente” (make haste slowly) as a framework for balancing urgency with careful consideration.

## “What Keeps You Up at Night” Responses

When asked about their primary concerns, panelists provided revealing insights:

**Anne Bouverot** expressed concern about job disruption and the need for comprehensive training, skilling, and upskilling programs to help people adapt to AI-driven changes.

**Vice Minister Shan** worried about various technical and social risks, including ensuring AI systems remain reliable and controllable while managing their broader societal impacts.

**Jennifer Bachus** focused on national security threats, particularly the risk of AI technologies being misused by hostile actors for surveillance and propaganda purposes.

**Chuen Hong Lew** was concerned about the pace of change, specifically asking “How do you re-skill such that the rate of change of human potential is faster than the rate of change of the algorithm?”

## Key Initiatives and Announcements

Several concrete initiatives were highlighted during the discussion:

– **AI Verified Open Source Foundation** – mentioned as part of collaborative efforts

– **Project Boonshot** – referenced as an innovative approach

– **Sustainable AI Coalition** – announced by France to promote AI for public good

– **European Investment Package** – 200 billion euros in investment with 400 million euros in specific commitments

– **DeepSeek** – cited by China as an example of cost-effective open-source AI development

## Common Themes

Despite different national approaches, several shared themes emerged:

– **Multi-stakeholder involvement** – All speakers acknowledged the need for collaboration between governments, private sector, researchers, and civil society

– **Innovation focus** – Each representative emphasized avoiding over-regulation that could stifle technological advancement

– **International cooperation** – All participants recognized AI’s global nature requires coordinated approaches

– **Human-centric development** – Speakers emphasized ensuring AI serves human welfare and addresses societal needs

## Conclusion

The discussion revealed both convergence and divergence in national AI governance approaches. While all participants agreed on the importance of fostering innovation and avoiding excessive regulation, they differed in their specific strategies, security concerns, and implementation mechanisms. The conversation highlighted the ongoing challenge of developing effective AI governance frameworks that can keep pace with rapid technological advancement while ensuring responsible development that serves the public good.

Moderator Gabriela Ramos concluded by noting the value of continuing such dialogues to advance international cooperation on AI governance, emphasizing the importance of maintaining open channels for discussion among diverse stakeholders.

Gabriela Ramos: Hello, everybody, and it’s great to be here, Doreen, Thomas, great to be in A.I. for good And to discuss what is in the mind of everybody, which is to get the maximum benefit from these technologies and reduce the risks And here we are accompanied by a fantastic panel because you will be sharing with us very different perspectives on how to achieve this goal And I’m sure that by the end of this conversation we will be able to learn a little bit how the way you handle the things can deliver good outcomes Because I guess that’s what we are all aiming at And we have with us, as was mentioned, Jennifer Bachus, acting head of the Bureau of Cybersecurity and Digital Policy in the USA Thank you, Jennifer We have Shan Zhongde, Mr. Vice Minister of Industry and Information Technology and Policy of China We were in the launch, welcome And we also have Chuen Hong Lew, Chief Executive Officer of Infocom Media Development Authority of India So it’s great to have you all And great to have you, Anne Bouverot, you know her, she’s the tech envoy of France And maybe I will give you a little bit of time, Anne, to get your breath and we will start with the US So, Jennifer, we know that there has been a change in the scope of how the current administration wants to deal with AI We know that now priority has been given to continue with the innovation ladder and try to minimize the question of regulations So, given that these technologies are global, I want to know how are you going to handle the fact that other areas like Europe or Japan, China Might have different approaches and you will need to engage with them So how would you do that?

Jennifer Bachus: Thank you and really what an honor to be here today and to be at this event Which actually I heard a lot about last year, all the excitement over the robots, the sort of fun and exciting And to be at really an event where you get to see the forefront of how multi-stakeholderism can play into AI governance And I think ultimately that is the point of view of the US government is that this needs to be a multi-stakeholder process And the United States has always been at the forefront of AI innovation driven by the strength of our free markets Our world-class research institutions and entrepreneurial spirit ultimately driven by the multi-stakeholders So it’s the policy of the United States to protect the US private sector innovation and technological leadership And to sustain and enhance America’s global AI leadership in order to promote human flourishing, economic competitiveness and national security And that’s why President Trump took very strong executive action to sign executive orders to roll back previous regulations on the AI industry The Trump administration believes that AI will have countless revolutionary applications in economic innovation, job creation, national security, healthcare, free expression and beyond All of which actually we keep hearing about every day at this conference And we think that restricting its development now would mean essentially paralyzing one of the most promising technologies we have seen in generations America wants partners. That is my message here today We want to embark on the AI revolution before us with the kind of spirit and openness and collaboration But to create that kind of trust, we need regulatory regimes around the world that foster the creation of AI technology rather than strangle it We need all of our friends, including here in Europe, to look to this new frontier with optimism Because that is why we are here today. AI for good The administration is troubled by reports that some governments are looking at tightening the screws on US tech companies with international footprints We won’t accept that and we think it’s a terrible mistake We need to focus now on the opportunity to unleash our most brilliant innovators and use AI to improve the well-being of our nations and that of our peoples The administration will assure that American AI technology continues to be the gold standard worldwide And we are the partner of choice for other foreign countries and certainly businesses as they expand their use of AI Excessive regulation of the AI sector could kill a transformative industry just as it’s taking off And we’ll make every effort to encourage pro-growth, deregulatory AI policies worldwide We really face an amazing, extraordinary prospect of a new industrial revolution But it won’t come to pass if over-regulation discourages innovation from taking the risk necessary for achievement Nor will it occur if we allow AI to become dominated by massive players looking to use the tech to censor or control users’ thoughts Thank you

Gabriela Ramos: Thank you, thank you very much, Jennifer I’m very pleased that you used the word over-regulation because that qualifies the whole debate And of course it’s the societal choices of where we want to put the limits But I think that this is where we want to put the current debate And now over to you, my dear Anne You were exceptional in the managing of the AI Action Summit We all were there I think that the way you framed it, President Macron framed it, was exactly to see how much we can join forces to get AI for good And to deliver good outcomes So I would like to bring this French perspective into the broader European perspective And how would you connect with what is happening in Europe and what is the French position between this regulation, innovation Which for me is a false dilemma, but over to you

Anne Bouverot: Thank you, thank you so much Can you hear me? Can you hear me? Yes, this is better Thank you so much for having me I’m sorry I was a little late to get on the stage It’s such a pleasure to be here and to be part of this panel and of this day And I really want to thank ITU and Doreen for this invitation And I’m delighted to be on this panel with you all In terms of the Paris AI Summit that we held in February After the previous summits that were held in Bletchley Park and before the next one in India We didn’t really want to focus on regulation We really wanted to focus on actions and practical outcomes And bringing people together on discussions and announcements On what we can do to make AI something that benefits people and societies So we made a few announcements, some at European level, some with different parameters There was a Europe announcement which was 200 billion euros of investment in Europe By companies and by the European Commission on European AI champions To promote the development and the deployment of AI in Europe But we also launched initiatives such as the current AI foundation Or initiatives to help develop public interest AI And in particular data sets or tool sets that will help make AI something more concretely There for projects that will benefit us all We had more than 400 million euros of commitments Mostly from philanthropies but also from countries and companies to this initiative And we also announced the sustainable AI coalition We now have close to 200 members, people who have joined And this is a coalition of the willing Again, none of this is regulation The focus was really on what we can do to promote innovation and action And in terms of Europe, you’re right, the word that often comes to mind in terms of Europe Is maybe more regulation than innovation I think things are changing and I very much welcome this change This is not about dismantling all the regulation But this is about really putting a much stronger focus on innovation This is what Europe has been doing with the AI continent plan With investments in research, investments in AI factories and AI gigafactories And I’m very pleased to see this new focus in Europe Great

Gabriela Ramos: Thank you so much. Let me then move to China and I feel that this is a very important representation of the leading countries that are advancing AI. We all were witness of DeepSeek, this major foundational model that was a fraction of the cost and also of the environmental impact and we know that this is being used for many applications, Deputy Minister. So I want to please ask you to share with our audience what are the latest developments in this area in China and how would you picture open source as an element to advance this innovation with inclusion and what are the perspectives there, please?

Shan Zhongde: Our chairman, President Xi, has paid great importance to the development of AI. I think this is the foundation of the revolution of industrialization in China. We are focusing for the green sustainable development and intelligent industrialization to construct a secure and a safe ecosystem in China and this is the purpose to build the sound ecosystem to promote the innovative and also to break through the foundation technology and the construct and also to come up with a large model representative such as a DeepSeek to contribute our power to the global development. Secondly is to empower the application among the industries such as to promote the manufacturing to green and sustainable development in such a case, especially for the petrochemical, steel, healthcare for those domains. We are trying to promote productivity, the quality of the product and a green sustainable development level. Thirdly, that is to reinforce the international collaboration based on the ITU and also to proactively share some of the strategies of China’s AI development. We also want to work together to promote the international standards of the AI. Also, we are open-minded and inclusive to construct the international standards framework for the open source. For open source, I think as a collaborative platform, it belongs to the global community. It’s a great power for the world and China is emphasizing on the open source governance and the development. First, it’s the industry need-driven and it’s for the academy and the research institutes and working together, we have a series of open sources systems through iterations coming out with new solutions. Second, working on the safety. We are setting up the guidelines and the standards and setting up for the risk prevention mechanisms and solidify the foundations. From China’s practices, a lot of businesses are already embracing AI, working with global business and to benefit from these advantages that we have this universal open economic and ecosystem and to hope to welcoming these challenges and opportunities. Thank you.

Gabriela Ramos: Thank you so much, Vice Minister, and yes, welcome the international cooperation and that’s why we are here with the under ITU ceiling, so great to have you. Maybe let’s then go to Singapore. Also, we have heard many speakers from your country sharing the very concrete agenda that you have on developing AI, looking at avoiding over-regulation and having a light touch kind of approach, but how would you advance this strategy? How would you share the main elements and what are the advantages and how would you grapple with the very fast pace in which these technologies are moving?

Chuen Hong Lew: Well, Gabriela, thank you so much. Really nice to see you again and likewise to all my counterparts here. We’ve had the pleasure to work together and of course it’s a real pleasure to be here. I’ve got to thank Doreen and ITU. That is a very interesting question and it’s not a light question at all, but maybe if I were to take a step back, as policy makers, all of us sitting here, what is it ultimately that we’re trying to aim for and achieve? For us, perhaps it’s not regulation per se, but if I may posit, it’s actually trust, because at the core of it, while the two do overlap, they’re not quite identical and for us, building a trusted ecosystem is at the core, because once you put just enough guardrails in place to ensure that a frontier technology like AI is used responsibly, it actually gives maximum space as far as utilisation and adoption is concerned. So for us, the difference here is that it is a virtuous cycle whereby the guardrails then remove derailers and give that maximum opportunity for innovation. So for us as a small country, that is that North Star that drives how we think about regulation, governance and so on. You talked about light touch. I was going to be slightly cheeky to say light touch actually requires extremely heavy lifting and the heavy lifting here is because it has to be viewed as building an entire ecosystem and not just about the laws and maybe if I elaborate on maybe three perspectives that might be useful. The first is to take a very science-based and evidence-based approach. You talked about how this space is moving very, very, very quickly. What you know today actually is very different from what was happening six months ago and I guarantee you six months from now, where the frontiers will be, will be very different. So investing in our digital trust centre, which is our modest effort at advancing the sciences as far as safety and governance is concerned, being part of the founding AI Safety Institute network, corralling the best minds, Turing Award winners, 100 over the best scientists from both east and west to come up with what we call the Singapore Consensus and R&D priorities. I think all of these are extremely critical to make sure that we stay at the forefront of where that testing and safety would be. But at the same time, we also want to get our hands dirty. Those of you who build AI models and we do that, realise that it is not so simple when it comes to testing and benchmarking and because we get our hands dirty, we kind of help bridge what the science can do, what the evidence can do and what real deployers out there in the industry actually face and come up with a practical approach. So that is our first perhaps pillar. The second pillar, working very closely with industry. I don’t think that this can be done working alone. We have an AI Verified Open Source Foundation. It gathers everything from model deployers to app developers to third-party testers. It has an open source toolkit. We think an open source toolkit called Project Boonshot is extremely critical, especially from a capacity building perspective as there are many other very small countries like Singapore that may not have the full wherewithal. Through that, because of that sandbox, because of that continuous iteration, we have distilled what we think are practical first steps. We don’t want to let good or rather we don’t want to let perfect get in the way of good and to issue practical guidelines associated with that. Both of these are very critical because it keeps us humble and honest that these things are moving very, very quickly. Perhaps the last piece is really I don’t think any of us sitting here would like to see the ecosystem fragment around the world. So a lot of efforts as far as standards, standards building. SC42, as some of you are familiar, we are leading a work effort to try and corral some of these as practical standards that industry can use. And of course, working with like-minded partners. This can be big tech. These are other countries. Through ASEAN, Singapore leads the ASEAN work group on AI, as well as the Digital Forum of Small States. FOSS is a big part of the UN ecosystem, 108 small countries and through hopefully that capacity building, we can bring everyone along. So if I kind of wrap up, ultimately we’re driven by the north star of that AI for good and if we can bring an overall balance. and the Zeitgeist is one of optimism. And we inoculate the population more broadly. When we say inoculate, means that get them to understand both the opportunities, but also be very clear about the risks. Then I think the overall benefits will outweigh some of the things that we’re worried about. And there is an optimism and opportunity that unfolds as a result of AI. Thank you.

Gabriela Ramos: Thank you so much, Chuen Hong. And as you have heard, we started with a very narrow question in terms of regulation, innovation, and all of the speakers have broadened this discussion to discuss about the ecosystem, to discuss about all of the elements that we bring together, including international cooperation, including working in a multi-stakeholder, including the industry, but also including the policies. And I come from that perspective because I have this weakness of being a policy wonk. And at the end is not only about understanding the technologies, but understanding the policy to outcomes and to see how do we incentivize these issues. And I’ve been working with UNESCO on the recommendation on the ethics of artificial intelligence. Now I’m running for the job in UNESCO. But at the end, I feel it’s very important that we dissect this discussion to really see all of the elements that bring us together. Because each one of your statements bring us closer together in how do we govern these technologies. Let me then get to the second question for the panel. And I think there is going to be, they call it, how they call it, my dear Doreen, mentee, mentee? There’s gonna be a mentee that you need to answer to while I also ask it to the speakers because I love this question that I’m gonna pose to you. And we’ll go from Anne, Vice Minister, Jennifer and Shan. We will go in this order. And then you will answer also in the public. What aspect of AI governance keeps you up at night? Anne.

Anne Bouverot: Thank you so much, Gabriela. Thank you for this. I’m lucky to go first because by the time everyone has spoken, we will have said everything. But let me say maybe two things so that I leave some things for everyone. The first one, we’ve all mentioned it. You exactly said that. This is inclusion and multi-stakeholder. But I’d like to speak about it from a perspective that is rooted in what AI is. What is AI? AI is really something, of course it’s a technology that the name dates back to the 1950s. But the more recent wave of AI is a development that stems from science and researchers. And that goes super quickly from researchers to engineers, to startups, to companies and becomes part of everyday life. So we’re faced with a technology that has research roots and very quick societal impacts. And I think if only for this reason, we need to involve in the global governance discussions researchers and we have Nobel Prizes and Turing Prizes and all sorts of researchers, economists as well and all the researchers. We need to involve engineers. We need to involve companies. That goes from startups to large companies, from little tech to big tech to middle tech. We absolutely need to have the private sector. Of course, we’re here, we need the governments and we need the governments from small, medium and large countries. I take this opportunity to commend Singapore on being such an AI-ready country. You’ve done some fantastic efforts for that. And also we need civil society. We need the people who express some fears, look at the risks and want to develop AI for the common good. So I guess I’m trying to explain why and give a few concrete ways in which I think this needs to be an inclusive multi-stakeholder governance. My second point is this is called how to make AI governance fit for purpose. What is the purpose? Sometimes we can have a tendency to say we need to make governance the best possible governance, but the main question is what for? What are we trying to do? We’re trying to make sure that this revolution that AI brings is something that can benefit to everyone and we want to minimize the risks. So we want to address the fact that there’s a very strong potential for disruption on jobs and we want to make sure we focus on the best actions, which for now seem to be training, skilling, upskilling people. Maybe there are others, but I think there’s a very important role of what should the governments be for the impact on jobs? What can countries do individually and they’re doing it and what should we do together? There’s the area of security, trust, safety where maybe there are things or there are things that we’re doing together to make sure that there is some testing. Again, we shouldn’t overdo it, but we should make sure we focus on the risks that are concrete and that we can work on. And there’s a number of other specific areas where we should, you say it really with a light touch and I like light touch, but I like focus. I think we should really try to think about what we want to do and how we do it. And that’s the purpose of governance. So those were my two points. I’m sure we will have lots of very interesting comments from my co-panelists.

Gabriela Ramos: I’m sure you don’t sleep at night because it’s really how to get it right. What Anne has just put together is how to get the whole thing right to deliver for people. Vice minister.

Shan Zhongde: Thank you. And the LLM existence has largely increased the AI’s capability for handling complex jobs and the AI’s applications used in different sectors. And there are several aspects of the AI governance for the future. One is how to balance the development and the safety. AI has become, and this is an advanced technology and coordinated development. It’s very urgent and it’s also a long-term plan. And how do we better deal with the different kind of risk that brought by the AI. As an example, AI is bringing this data leakage and these model hallucinations risks and AI activities and the consequences and these problem with the relationships between people and also job impacts and the social structural changes. And the number three, how do we do better global governance? And AI is bringing this intelligence divide will increase the development level among countries. This AI governance needing us to join hands from with everyone, all states. And we China will be like a people centered AI for good and we can traceable and reliable and an AI that we can monitor. And we have this different classifications and under our control and the monitoring. One is to increase the innovation. Second is for the universal development and to increase its connection with the infrastructure, promote innovations and for promote applications and allow for the economic sustainable development. Second is to get the sectors consensus to increase of our intelligence, increase exchanges with all countries for AI for good and to come up to consensus and explore a equitable and inclusive governance models and the deepening of our international corporations going for AI for good and have a mindset for collaborations, open and sharing and the win-win situations and the practice and the work together and the share together and the build together. And we set up the international standards and the use these standards for applications and the promote AI for good and we hope. all the countries that we can work together on the international exchanges and the conversations and to make sure A.I. will be benefited for the human society. Thank you.

Gabriela Ramos: Thank you very much, Vice Minister. Jennifer, over to you.

Jennifer Bachus: So, in addition to my very strong concern that essentially A.I. governance is going to strangle A.I. in its bed, for lack of a better term, I think the United States is also concerned is that authoritarian regimes are stealing and using A.I. to strengthen militaries, intelligence and surveillance capabilities, capturing foreign data, creating propaganda to undermine other nations’ national security, and violating human rights, and that these discussions about A.I. governance are going to be utilized to essentially enhance these abilities and enhance this effort. The United States is going to block such efforts. We’re going to safeguard American A.I. and chip technologies from theft and misuse, and we’re going to work with our allies and partners to strengthen and extend these protections and close pathways to adversaries attaining A.I. capabilities that essentially threaten all of our peoples. I would be remiss if I didn’t say that essentially A.I. can be used for incredible good, but it can also be used for incredible harm, and we need to think about A.I. governance in that mindset. So, we are committed to making sure that our A.I. is the gold standard and that we’re the partners of choice, and that as we go forward in trying to create A.I. governance models, we continue to have a private sector, multi-stakeholder-led approach that’s bottom-up, that essentially creates the innovation that we all need to address the challenges that we’re facing today. Thank you. Thank you.

Gabriela Ramos: Thank you so much, Jennifer. Mr. Chuen Hong Lew. Please, please, please, please.

Chuen Hong Lew: I try to sleep well at night. I think that’s very important. So, if all of you are not getting your seven hours, please do. I think it’s medically proven, but jokes aside, perhaps two thoughts. You know, there’s a famous saying. It was by Jack Welch, and he says, when the rate of change outside is greater than the rate of change inside, the end is near. And I think when I express this, the idea is the sheer speed of which A.I. is progressing, and the sheer unknown, and that uncertainty, I think probably keeps not just myself, but I think everybody in the audience here awake. There is probably a visceral sense that this thing is happening much faster, that we have the ability to either cope or to address, and I think that’s something that is always at the top of my mind, and I think at the top of the mind of a very small country like Singapore. But here is where there is a bit of an oxymoron. I’m not a Latin expert, but I’ve always liked this Latin term, and this Latin term is called festinilente, and it basically means make haste slowly. So, it’s about holding that conflicting thought at the back of your mind, that even as the frontier technology moves very quickly, and we must run fast, we must run fast, because I think it behooves all of us here. I’ve seen frontier technology come and go, but I think this is one of those things that will fundamentally reshape what society is, and therefore the ability to run fast, to maximise the opportunities, as well as to mitigate the risks, but do so deliberately, not to knee-jerk, not to be impulsive. So, I think the first thing at the back of my mind is festinilente, make haste slowly. The second thought which is related to this is how do we make sure that AI is harnessed in a human-centric way for the public good? Because at the end of the day, after we talk about AI, ultimately does it lead to human public good? On one end of the dystopian sort of spectrum, you can say AI is going to replace all of us here today, and I’m very sure at the back of all your minds there’s that little niggling doubt, but if you go to the other end of the utopian spectrum, there is a potential embarrassment of riches, where the AI is able to, as an agent, do everything from booking your travel programme to making a research for you, and that allows us to at least sleep 10 hours every night. But I think the answer obviously is somewhere in between, and within here, maybe the second thought at the back of my mind is talent, and talent writ large with a capital T, because if you really want for all of us here, be it small countries, large countries, to maximum the benefits as far as jobs are concerned, there is a fear that AI is going to replace a lot of entry-level jobs, for example. How do you transition? How do you train? These are not trivial questions. They may sound mundane, but they are extremely critical. How do you re-skill? How do you re-skill such that the rate of change of human potential is faster than the rate of change of the algorithm? I think that is critical to make sure that we maximise the potential, so talent, and talent when written small also means that we all know big tech companies are hoovering up talent, tens of millions of dollars as far as signing bonuses. How do we make sure that some of this talent, and we can attract this talent, to the larger purpose of understanding its broader impact, contributing to public service? For us to be able to keep at the forefront, to look big tech eye to eye, I need to know, and we need to know how AI is actually deployed. So I think the idea of talent, and how we groom that talent, how do we attract that talent, how do we harness that talent for public good, I think is probably the second thing at the back of my mind. Hope that is useful.

Gabriela Ramos: That is very, very useful, and I want to thank you all, because at the end, with very different perspectives, what we are hearing in this conversation is that we need to better fit ourselves to deal with AI. It is not about AI, as you said, I feel that we have seen the pace in which this is developing. You all mentioned the impact in labor markets and skills. At the end, we were thinking that it was going to be the routine works that were going to be replaced, and now we know that with generative AI, a higher level of functions are being endangered. We know the question of those that are lagging behind. We still have one third of the world that doesn’t have access to a stable internet. We know that there is this dual use, the misuse, the manipulation, the abuse, the intentional using of AI for producing harm, which we should not neglect, but we also know that the AI revolution is challenging the structures that we have in the governance fields as we know it, and at the end, I feel that one of the real questions is how much we invest in those governance structures in the very same, in the sovereign way in which each country wants to determine their own purposes, but at the end is this adaptability that we need to get together. But we are very pleased to have this very impressive group of speakers, because each one of you can show where do you put the emphasis? Do you put it in the investments, innovation? Do you put it in the multi-stakeholder? Do you put it on the frameworks? Do you put it on making sure that everybody benefits? And I feel that this is the kind of reflections that we want to have. And just to conclude, I don’t know if we need to see the results of the question or not. Do we have it? I’m calling on the technicians. No, maybe we will be looking into that when we, how to make it, no. We don’t have them, but we will look into it. In any case, I feel that we have a lot of very interesting comments from our speakers. From my perspective, it’s about incentives, it’s about investments, it’s about institutions. And I think that each one of you have contributed to these debates. And I just want to finish by thanking ITU, Doreen, and all of you for joining us in this conversation that will continue. Because as you mentioned, the pace is very fast, and we need to learn and mention also the question of research, understanding better, and the collaboration that we all need to bring our countries together to work to get it right. So thank you so much. And let’s continue with the other panels. Thank you so much.

Agreement points

Multi-stakeholder approach is essential for AI governance

Speakers

– Jennifer Bachus
– Anne Bouverot
– Shan Zhongde
– Chuen Hong Lew

Arguments

Multi-stakeholder process is essential for AI governance, driven by free markets and entrepreneurial spirit

Multi-stakeholder governance must include researchers, engineers, companies, governments, and civil society

International collaboration through ITU and working together on global AI standards is essential

Global ecosystem should not fragment, requiring standards building and working with like-minded partners

Summary

All speakers agree that AI governance requires inclusive participation from multiple stakeholders including governments, private sector, researchers, civil society, and international organizations. They emphasize the need for collaborative approaches rather than top-down regulatory frameworks.

Topics

Legal and regulatory | Infrastructure

Innovation should be prioritized over excessive regulation

Speakers

– Jennifer Bachus
– Anne Bouverot
– Chuen Hong Lew

Arguments

Excessive regulation could kill transformative AI industry and discourage necessary risk-taking for innovation

Focus should be on actions and practical outcomes rather than regulation, with emphasis on innovation over regulatory constraints

Light-touch regulation requires heavy lifting to build entire ecosystems, not just laws

Summary

These speakers share the view that over-regulation poses a significant threat to AI innovation and that governance approaches should prioritize enabling innovation while maintaining appropriate safeguards.

Topics

Legal and regulatory | Economic

International cooperation and standards are crucial for AI governance

Speakers

– Jennifer Bachus
– Anne Bouverot
– Shan Zhongde
– Chuen Hong Lew
– Gabriela Ramos

Arguments

America wants partners but needs regulatory regimes that foster rather than strangle AI technology creation

Multi-stakeholder governance must include researchers, engineers, companies, governments, and civil society

International collaboration through ITU and working together on global AI standards is essential

Global ecosystem should not fragment, requiring standards building and working with like-minded partners

International cooperation essential for managing global nature of AI technologies

Summary

All speakers recognize that AI’s global nature requires international cooperation and coordination, though they may differ on specific approaches. They agree on the need for shared standards and collaborative frameworks.

Topics

Legal and regulatory | Infrastructure

AI has significant economic and social impacts that require attention

Speakers

– Jennifer Bachus
– Anne Bouverot
– Shan Zhongde
– Chuen Hong Lew
– Gabriela Ramos

Arguments

AI will have revolutionary applications in economic innovation, job creation, healthcare, and beyond

Focus needed on job disruption mitigation through training, skilling, and upskilling programs

AI governance should promote economic sustainable development and universal benefits

Talent development and retention is critical for maximizing AI benefits while ensuring human-centric public good

Comprehensive approach needed addressing labor market impacts, digital divides, and misuse concerns

Summary

All speakers acknowledge that AI will have profound impacts on jobs, economy, and society, requiring proactive measures to manage transitions and ensure benefits are widely distributed.

Topics

Economic | Development

Similar viewpoints

Both speakers emphasize the need to move away from regulatory-heavy approaches toward innovation-focused strategies, with Anne noting Europe’s shift in this direction aligning with the US position.

Speakers

– Jennifer Bachus
– Anne Bouverot

Arguments

Excessive regulation could kill transformative AI industry and discourage necessary risk-taking for innovation

Europe is shifting focus from regulation to innovation with investments in AI champions and practical initiatives

Topics

Legal and regulatory | Economic

Both speakers advocate for balanced approaches that enable innovation while maintaining safety and trust through appropriate monitoring and evidence-based frameworks.

Speakers

– Shan Zhongde
– Chuen Hong Lew

Arguments

Balance between development and safety is crucial, requiring people-centered AI that is traceable, reliable and monitorable

Trust-building through guardrails enables maximum innovation space, requiring science-based and evidence-based approaches

Topics

Legal and regulatory | Human rights

Both speakers prefer practical, action-oriented approaches over traditional regulatory frameworks, emphasizing the complexity of creating effective governance ecosystems.

Speakers

– Anne Bouverot
– Chuen Hong Lew

Arguments

Focus should be on actions and practical outcomes rather than regulation, with emphasis on innovation over regulatory constraints

Light-touch regulation requires heavy lifting to build entire ecosystems, not just laws

Topics

Legal and regulatory | Infrastructure

Unexpected consensus

Shared concern about AI development pace exceeding governance capabilities

Speakers

– Chuen Hong Lew
– Gabriela Ramos

Arguments

Rate of AI change exceeds ability to cope, requiring deliberate action despite rapid progress

AI revolution challenges existing governance structures and requires investment in adaptability

Explanation

Despite representing different perspectives (Singapore’s tech-forward approach vs. UNESCO’s ethics-focused framework), both speakers acknowledge the fundamental challenge that AI development is outpacing institutional capacity to govern it effectively.

Topics

Legal and regulatory | Development

Recognition of AI’s dual-use nature and security concerns

Speakers

– Jennifer Bachus
– Shan Zhongde

Arguments

Authoritarian regimes stealing AI for military, intelligence, surveillance and propaganda purposes threatens national security

AI brings risks including data leakage, model hallucinations, and social structural changes that need monitoring

Explanation

Despite representing potentially competing geopolitical interests (US and China), both speakers acknowledge legitimate security concerns around AI misuse, though they frame the threats differently.

Topics

Cybersecurity | Human rights

Emphasis on human-centric AI development

Speakers

– Shan Zhongde
– Chuen Hong Lew

Arguments

Balance between development and safety is crucial, requiring people-centered AI that is traceable, reliable and monitorable

Talent development and retention is critical for maximizing AI benefits while ensuring human-centric public good

Explanation

Both China and Singapore, despite different governance systems, emphasize human-centric approaches to AI development, suggesting convergence on fundamental values around AI serving human welfare.

Topics

Human rights | Development

Overall assessment

Summary

The speakers demonstrated remarkable consensus on key principles including the need for multi-stakeholder governance, international cooperation, innovation-focused approaches over heavy regulation, and attention to AI’s economic and social impacts. Despite representing different countries and governance philosophies, they converged on fundamental challenges and approaches.

Consensus level

High level of consensus on principles and challenges, with differences mainly in emphasis and implementation approaches rather than fundamental disagreements. This suggests potential for effective international cooperation on AI governance frameworks, though specific policy details may require further negotiation. The consensus indicates a maturing global understanding of AI governance needs that transcends traditional geopolitical divisions.

Different viewpoints

Regulatory approach to AI governance

Speakers

– Jennifer Bachus
– Anne Bouverot
– Shan Zhongde
– Chuen Hong Lew

Arguments

Excessive regulation could kill transformative AI industry and discourage necessary risk-taking for innovation

Focus should be on actions and practical outcomes rather than regulation, with emphasis on innovation over regulatory constraints

Balance between development and safety is crucial, requiring people-centered AI that is traceable, reliable and monitorable

Trust-building through guardrails enables maximum innovation space, requiring science-based and evidence-based approaches

Summary

The US strongly opposes regulation and advocates for deregulation, while China emphasizes balanced approach with monitoring and control mechanisms. France/Europe focuses on practical outcomes over regulation but acknowledges some regulatory framework. Singapore advocates for light-touch regulation through trust-building guardrails.

Topics

Legal and regulatory | Economic

Security concerns and threat assessment

Speakers

– Jennifer Bachus
– Shan Zhongde

Arguments

Authoritarian regimes stealing AI for military, intelligence, surveillance and propaganda purposes threatens national security

International collaboration through ITU and working together on global AI standards is essential

Summary

The US explicitly identifies authoritarian regimes as threats that steal AI technology for harmful purposes, while China advocates for open international collaboration and standards-setting, representing fundamentally different security perspectives.

Topics

Cybersecurity | Legal and regulatory

Role of international cooperation and standards

Speakers

– Jennifer Bachus
– Shan Zhongde
– Chuen Hong Lew

Arguments

America wants partners but needs regulatory regimes that foster rather than strangle AI technology creation

International collaboration through ITU and working together on global AI standards is essential

Global ecosystem should not fragment, requiring standards building and working with like-minded partners

Summary

The US wants cooperation but on its terms with pro-growth, deregulatory policies, while China emphasizes inclusive global collaboration through ITU. Singapore focuses on preventing fragmentation through standards but works with ‘like-minded partners’, suggesting selective cooperation.

Topics

Legal and regulatory | Infrastructure

Unexpected differences

Open source AI development approach

Speakers

– Jennifer Bachus
– Shan Zhongde
– Chuen Hong Lew

Arguments

America wants partners but needs regulatory regimes that foster rather than strangle AI technology creation

International collaboration through ITU and working together on global AI standards is essential

Global ecosystem should not fragment, requiring standards building and working with like-minded partners

Explanation

While China explicitly promotes open source governance and development (mentioning DeepSeek as an example) and Singapore has an open source foundation, the US position focuses on protecting American AI technology from theft and misuse, creating an unexpected tension around openness versus protection in AI development.

Topics

Legal and regulatory | Infrastructure

Speed of AI development response

Speakers

– Jennifer Bachus
– Chuen Hong Lew

Arguments

Excessive regulation could kill transformative AI industry and discourage necessary risk-taking for innovation

Rate of AI change exceeds ability to cope, requiring deliberate action despite rapid progress

Explanation

Unexpectedly, while both acknowledge the rapid pace of AI development, they draw opposite conclusions. The US uses speed as an argument against regulation to avoid stifling innovation, while Singapore uses the same speed concern to argue for more deliberate, careful action following ‘make haste slowly’ principle.

Topics

Legal and regulatory | Development

Overall assessment

Summary

The discussion reveals significant philosophical differences in AI governance approaches, with the US advocating for minimal regulation and market-driven solutions, China promoting balanced development with monitoring mechanisms, and smaller countries like Singapore seeking middle-ground approaches through trust-building and light-touch regulation.

Disagreement level

Moderate to high disagreement level with important implications for global AI governance. The fundamental tension between the US anti-regulatory stance and other countries’ more structured approaches, combined with security concerns and different views on international cooperation, suggests that achieving unified global AI governance will be challenging and may require compromise or parallel frameworks.

Partial agreements

Similar viewpoints

Both speakers emphasize the need to move away from regulatory-heavy approaches toward innovation-focused strategies, with Anne noting Europe’s shift in this direction aligning with the US position.

Speakers

– Jennifer Bachus
– Anne Bouverot

Arguments

Excessive regulation could kill transformative AI industry and discourage necessary risk-taking for innovation

Europe is shifting focus from regulation to innovation with investments in AI champions and practical initiatives

Topics

Legal and regulatory | Economic

Both speakers advocate for balanced approaches that enable innovation while maintaining safety and trust through appropriate monitoring and evidence-based frameworks.

Speakers

– Shan Zhongde
– Chuen Hong Lew

Arguments

Balance between development and safety is crucial, requiring people-centered AI that is traceable, reliable and monitorable

Trust-building through guardrails enables maximum innovation space, requiring science-based and evidence-based approaches

Topics

Legal and regulatory | Human rights

Both speakers prefer practical, action-oriented approaches over traditional regulatory frameworks, emphasizing the complexity of creating effective governance ecosystems.

Speakers

– Anne Bouverot
– Chuen Hong Lew

Arguments

Focus should be on actions and practical outcomes rather than regulation, with emphasis on innovation over regulatory constraints

Light-touch regulation requires heavy lifting to build entire ecosystems, not just laws

Topics

Legal and regulatory | Infrastructure

Key takeaways

AI governance requires a multi-stakeholder approach involving governments, private sector, researchers, civil society, and international organizations

There is broad consensus that excessive regulation could stifle AI innovation, with preference for light-touch, trust-building approaches over heavy regulatory frameworks

International cooperation and standardization are essential given AI’s global nature, despite different national approaches

The rapid pace of AI development creates challenges for governance structures, requiring adaptive and evidence-based policy approaches

Talent development, reskilling, and addressing job displacement are critical priorities for maximizing AI benefits while mitigating social disruption

Security concerns about AI misuse by authoritarian regimes for surveillance, military, and propaganda purposes need to be addressed

Focus should be on practical outcomes and actions rather than theoretical regulatory frameworks

Small and developing countries need capacity building and inclusive approaches to prevent an ‘intelligence divide’

Resolutions and action items

Continue international collaboration through ITU and other multilateral frameworks for AI standards development

Invest in talent development, training, and reskilling programs to address job displacement concerns

Develop open-source toolkits and capacity building initiatives for smaller countries

Maintain science-based and evidence-based approaches to AI governance that can adapt to rapid technological changes

Strengthen partnerships between like-minded countries while protecting AI technologies from misuse by adversaries

Unresolved issues

How to effectively balance innovation promotion with necessary safety guardrails across different national contexts

Specific mechanisms for preventing AI technology theft and misuse by authoritarian regimes

Concrete strategies for addressing the ‘intelligence divide’ between developed and developing nations

Detailed approaches for managing AI’s impact on labor markets and social structures

How to maintain multi-stakeholder governance while addressing legitimate national security concerns

Practical implementation of international AI standards given different regulatory philosophies

Suggested compromises

Focus on ‘light-touch’ regulation that builds trust through targeted guardrails rather than comprehensive restrictions

Emphasize practical actions and outcomes over theoretical regulatory frameworks

Pursue innovation-focused policies while maintaining necessary safety measures

Develop inclusive governance models that accommodate different national approaches while promoting international cooperation

Balance private sector leadership with appropriate government oversight and civil society input

I’m very pleased that you used the word over-regulation because that qualifies the whole debate… this is where we want to put the current debate

Speaker

Gabriela Ramos

Reason

This comment reframes the entire discussion by distinguishing between ‘regulation’ and ‘over-regulation,’ suggesting that the debate isn’t about whether to regulate AI but about finding the right balance. It acknowledges that some regulation may be necessary while avoiding stifling innovation.

Impact

This comment shifted the conversation away from a binary regulation vs. innovation debate toward a more nuanced discussion about appropriate governance levels. It allowed subsequent speakers to move beyond defensive positions and engage with the complexity of balancing innovation with necessary safeguards.

For us, perhaps it’s not regulation per se, but if I may posit, it’s actually trust, because at the core of it… building a trusted ecosystem is at the core

Speaker

Chuen Hong Lew

Reason

This insight fundamentally reframes the governance challenge from a regulatory compliance issue to a trust-building exercise. It suggests that the goal isn’t regulation for its own sake, but creating conditions where AI can be deployed with confidence and social acceptance.

Impact

This comment elevated the discussion to a more philosophical level, moving beyond technical regulatory approaches to consider the underlying social contract needed for AI adoption. It influenced how other panelists framed their responses, with several subsequently emphasizing collaboration and multi-stakeholder approaches.

Light touch actually requires extremely heavy lifting and the heavy lifting here is because it has to be viewed as building an entire ecosystem

Speaker

Chuen Hong Lew

Reason

This paradoxical statement reveals the complexity behind seemingly simple regulatory approaches. It challenges the assumption that ‘light touch’ regulation is easier or less comprehensive, instead suggesting it requires more sophisticated, systemic thinking.

Impact

This comment deepened the conversation by revealing the hidden complexity in governance approaches. It helped explain why effective AI governance requires coordination across multiple domains (science, industry, standards, international cooperation) rather than simple rule-making.

What is AI? AI is really something… that has research roots and very quick societal impacts… we need to involve in the global governance discussions researchers… engineers… companies… governments… civil society

Speaker

Anne Bouverot

Reason

This comment provides a foundational analysis of why AI governance is uniquely challenging – because it rapidly transitions from academic research to societal impact. It logically derives the need for multi-stakeholder governance from the nature of AI itself.

Impact

This insight provided intellectual grounding for the multi-stakeholder approach that all panelists endorsed. It moved the discussion from advocating for inclusion to explaining why inclusion is structurally necessary for AI governance, making the argument more compelling.

When the rate of change outside is greater than the rate of change inside, the end is near… festina lente… make haste slowly

Speaker

Chuen Hong Lew

Reason

This comment captures the central tension in AI governance – the need to move quickly enough to keep pace with technological development while being deliberate enough to avoid mistakes. The Latin phrase ‘festina lente’ elegantly encapsulates this paradox.

Impact

This philosophical insight resonated throughout the discussion, providing a framework for understanding why AI governance feels so challenging. It validated the urgency felt by all participants while advocating for thoughtful approaches, helping to reconcile the apparent contradiction between speed and deliberation.

How do you re-skill such that the rate of change of human potential is faster than the rate of change of the algorithm?

Speaker

Chuen Hong Lew

Reason

This question reframes the jobs/displacement challenge in mathematical terms, suggesting that the solution isn’t to slow down AI development but to accelerate human adaptation. It’s a profound reconceptualization of the human-AI relationship.

Impact

This insight shifted the discussion from defensive concerns about job displacement to proactive thinking about human development. It influenced the moderator’s closing remarks about the need to ‘better fit ourselves to deal with AI’ rather than constraining AI to fit existing structures.

Overall assessment

These key comments fundamentally elevated the discussion from a typical regulatory debate to a sophisticated exploration of governance philosophy and systemic challenges. Rather than participants defending predetermined positions, the insightful comments created space for nuanced thinking about trust, ecosystem building, and the unique characteristics of AI that demand new governance approaches. The discussion evolved from binary thinking (regulation vs. innovation) to complex, multi-dimensional analysis of how societies can adapt to transformative technology. The most impactful comments were those that provided new conceptual frameworks – like trust over regulation, ecosystem thinking, and the paradox of making haste slowly – that allowed all participants to engage more thoughtfully with the challenges rather than simply advocating for their national positions.

How to balance AI development and safety in the face of rapid technological advancement

Speaker

Shan Zhongde

Explanation

The Vice Minister highlighted this as a critical governance challenge, noting the urgency of coordinated development while managing various risks including data leakage, model hallucinations, and social structural changes

How to address the AI intelligence divide that will increase development gaps between countries

Speaker

Shan Zhongde

Explanation

This was identified as a key concern requiring global cooperation to ensure equitable and inclusive AI governance models

How to ensure the rate of change of human potential is faster than the rate of change of algorithms

Speaker

Chuen Hong Lew

Explanation

This addresses the critical challenge of human adaptation to AI advancement, particularly in terms of reskilling and training to prevent job displacement

How to attract and retain AI talent for public service when big tech companies are offering substantial compensation packages

Speaker

Chuen Hong Lew

Explanation

This is essential for governments to maintain oversight capabilities and develop AI governance frameworks that can effectively regulate the technology

How to transition and retrain workers whose jobs may be displaced by AI, particularly entry-level positions

Speaker

Chuen Hong Lew

Explanation

This addresses the practical implementation challenges of managing AI’s impact on employment and ensuring human-centric AI development

How to prevent authoritarian regimes from using AI to strengthen military, intelligence and surveillance capabilities

Speaker

Jennifer Bachus

Explanation

This represents a national security concern about the dual-use nature of AI technology and the need for protective measures

How to develop practical testing and benchmarking standards for AI models that bridge scientific research with real-world deployment challenges

Speaker

Chuen Hong Lew

Explanation

This addresses the gap between theoretical AI safety research and practical implementation needs for industry deployers

How to ensure AI governance remains inclusive and multi-stakeholder while managing the rapid pace of technological change

Speaker

Anne Bouverot

Explanation

This addresses the challenge of maintaining broad participation in governance discussions when the technology is evolving faster than traditional policy-making processes