Day 0 Event #172 Major challenges and gaps in intelligent society governance

Summary

This discussion focused on the development and governance of intelligent societies, exploring various aspects of AI and its impact on global development. The speakers addressed China’s objectives in building an intelligent society, emphasizing the importance of human-centered approaches and ethical considerations. They highlighted the need for international cooperation in addressing challenges such as energy consumption and environmental impacts of AI development.

The discussion explored the paradigm shift in AI governance, noting the transition from material technological subjects to human-like technological subjects, and the importance of flexible, open governance frameworks. Speakers emphasized the role of standardization in addressing opportunities and challenges in building intelligent social culture and civilization.

The potential of AI in addressing global issues like climate change and achieving sustainable development goals was discussed, with a focus on leveraging AI for social progress while maintaining human values and rights. The importance of interdisciplinary approaches and global cooperation in AI governance was stressed, with speakers calling for diverse, multidisciplinary panels to guide AI development.

Speakers also addressed the need for transparency in AI decision-making, the reshaping of knowledge production and social structures by generative AI, and the importance of aligning AI with human values. The discussion highlighted the transformative potential of AI across various sectors, including healthcare, education, and public services, while also acknowledging the need for responsible development and governance.

Overall, the discussion underscored the complex interplay between technological advancement, social impact, and governance challenges in the development of intelligent societies, emphasizing the need for collaborative, human-centered approaches to harness the benefits of AI while mitigating potential risks.

Keypoints

Major discussion points:

– China’s national plans and actions for building an intelligent society

– International governance challenges for AI, especially related to energy use and environmental impacts

– Philosophical and societal implications of generative AI and cognitive computing systems

– Governance transformation and standardization needed for the intelligent society

– Ensuring AI development is human-centered and supports sustainable development goals

Overall purpose:

The purpose of this discussion was to explore various perspectives on the development of intelligent societies powered by AI, examining both the opportunities and challenges from technological, governance, philosophical and global development standpoints. The speakers aimed to provide insights on how to responsibly advance AI while addressing key issues like environmental impacts, ethics, and human values.

Tone:

The overall tone was academic and forward-looking. Speakers presented research findings and policy recommendations in a formal, analytical manner. There was a sense of cautious optimism about AI’s potential balanced with calls for responsible governance and development. The tone remained consistent throughout, with each speaker building on previous points while adding their own area of expertise to the discussion.

Speakers

– Yuming Wei: Professor at Tsinghua University, moderator of the session

– Gong Ke: Former president of the World Federation of Engineering Organizations, executive director of Chinese Institute of New Generation Artificial Intelligence Development Strategies, former president of Nankai University

– Kevin C. Desuoza: Professor of Business, Technology, and Strategy at the School of Business, Queensland University of Technology

– Ru Peng: Professor at the School of Public Policy and Management, Tsinghua University

– Sam Daws: Senior advisor at the Oxford Martin AI Governance Initiative, Oxford University

– Min Jianing: Professor at Harbin Institute of Technology and Editor-in-Chief of the Journal of Public Administration

– Poncelet Ileleji: CEO of Jack Cook Labs, Banjul, Gambia

Additional speakers:

– Suf Gongke: Former president of the World Federation of Engineering Organizations, executive director of Chinese Institute of New Generation Artificial Intelligence Development Strategies, former president of Nankai University (likely the same person as Gong Ke, with a slight name variation)

The Development and Governance of Intelligent Societies: A Comprehensive Overview

This discussion, moderated by Professor Yuming Wei of Tsinghua University, brought together experts from various fields to explore the multifaceted aspects of developing and governing intelligent societies powered by artificial intelligence (AI). The session featured a mix of in-person and online presentations, addressing a wide range of topics from national strategies to global governance challenges.

China’s National Strategy for an Intelligent Society

Gong Ke, former president of the World Federation of Engineering Organizations and executive director of the Chinese Institute of New Generation Artificial Intelligence Development Strategies, outlined China’s comprehensive national plan for AI development. He introduced the “1-2-3-4 planning” framework:

1. One overarching goal: building an intelligent society

2. Two driving forces: technological innovation and institutional innovation

3. Three stages of development: 2020, 2025, and 2030 milestones

4. Four key areas: social services, social governance, infrastructure, and public safety

This approach demonstrates China’s commitment to leveraging AI for societal advancement while addressing potential challenges.

Global Governance and International Collaboration

Sam Daws from Oxford University emphasized the critical importance of global governance and international collaboration in addressing AI challenges. He highlighted several key points:

1. The need for interoperable approaches to sustainable AI development

2. Opportunities for collaboration leading up to COP30 in 2025

3. The role of the UN Technology Envoy in facilitating international cooperation

4. The importance of addressing AI’s environmental impact, including high energy and water consumption

Daws introduced the concept of the Jevons Paradox to explain why AI energy use continues to rise despite efficiency gains, suggesting that increased efficiency may lead to increased overall consumption.

Societal Impact and Ethical Considerations

Min Jianing, Professor at Harbin Institute of Technology, presented 10 epidemiological questions on generative AI, exploring how large-language models are transforming knowledge production, decision-making processes, and social structures. His presentation covered:

1. The intrinsic mechanisms of evolution in knowledge production triggered by AI models

2. Potential reshaping of human society and relations of production

3. The impact of AI on human nature and existing beliefs

4. The possibility of value upgrades through openness and neutral learning

Kevin C. Desuoza from Queensland University of Technology discussed cognitive computing systems and their role in public value creation. He highlighted how AI is transforming approaches to societal challenges, citing examples of technology companies shifting focus from healthcare to “healthiness.” Despite technical difficulties with slide presentation, Desuoza’s insights broadened the conversation to consider how AI might transform entire paradigms of thinking about social issues.

Governance Transformation and Standardisation

Ru Peng, Professor at Tsinghua University, stressed the importance of standardisation as a tool for AI governance. She argued that standardisation is not merely a technical process but a multifaceted approach with strategic, social, and people-oriented dimensions. Peng called for the development of key standards in areas such as:

1. Social application for generative AI technology

2. Smart healthcare

3. Smart justice

4. Smart grassroots governance

She also mentioned China’s 92 national intelligent social governance experimental bases, highlighting the country’s practical approach to exploring AI governance models.

Sustainable Development and Human-Centered AI

Poncelet Ileleji from Jack Cook Labs in Gambia emphasized AI’s potential role in achieving the UN Sustainable Development Goals. He stressed the importance of a human-centered approach to AI development, referencing the UN AI advisory board recommendations. Ileleji’s presentation highlighted the need for AI applications that address global challenges while maintaining focus on human values and ethics.

Unresolved Issues and Future Directions

The discussion highlighted several unresolved issues and areas for future research and policy development:

1. Specific mechanisms for global interoperable approaches to sustainable AI development

2. Balancing national interests and global collaboration in AI governance

3. Concrete steps to align AI development with human values and ethics across different cultural contexts

4. Methods to effectively measure and mitigate the energy and environmental impacts of AI systems

Conclusion

This comprehensive discussion underscored the complex interplay between technological advancement, social impact, and governance challenges in the development of intelligent societies. The speakers emphasized the need for collaborative, human-centered approaches to harness the benefits of AI while mitigating potential risks. As AI continues to reshape various sectors, including healthcare, education, and public services, the importance of responsible development and governance becomes increasingly apparent.

The moderator, Professor Yuming Wei, concluded by noting the complementary nature of the speakers’ topics, highlighting how the diverse perspectives contributed to a holistic understanding of intelligent societies’ development and governance. Despite occasional technical difficulties, the session provided valuable insights into the multifaceted nature of AI development and governance, emphasizing the need for continued dialogue, research, and international cooperation to address the challenges and opportunities presented by intelligent societies.

Yuming Wei: Okay, good afternoon distinguished guests, esteemed colleagues and friends, ladies and gentlemen. It’s my great honor to welcome all of you to this session. I am Wei Yu Lin from Tsinghua University. On behalf of the organizing committee, I would like to express my deep respect and gratitude to all of you for joining us today and contributing to this important discussion. Today we are gathered at a pivotal moment in history. The rapid development of artificial intelligence is driving huge transformation towards intelligent society, bolstering new academic frontiers, technological breakthroughs, and innovative models. This transformation brings enormous opportunities for development across all sectors, however it also introduces a range of complex governance challenges, including ethical concerns, social inequity, privacy, and security risks. This session aims to address these pressing issues from a global perspective, analyzing the latest trends, major challenges, and the future opportunities in the development of intelligent society. Through the lens of government and international collaboration, we will explore experimental and adaptive approaches needed to navigate the governance transition of intelligent society. We are privileged to have an exceptional panel of speakers, each of whom bring unique expertise and global perspective to this discussion. Now allow me to introduce them. Suf Gongke, former president of the World Federation of Engineering Organizations, executive director of Chinese Institute of New Generation Artificial Intelligence Development Strategies, and former president of Nankai University. Mr. Sam Zhou, senior advisor at the Oxford Martin AI Governance Initiative, Oxford University. University, and Director of Multilateral AI. Prof. Min Jianing, Professor at Harbin Institute of Technology and Editor-in-Chief of the Journal of Public Administration. Prof. Kevin D’Souza, Professor of Business, Technology, and Strategy at the School of Business, Queensland University of Technology. Prof. Ru Peng, Professor at the School of Public Policy and Management, Tsinghua University. And Mr. Pencilit Eladji, CEO of Jack Cook Labs, Banjul, Gambia, in Africa. To begin our session, it is my distinct pleasure to introduce our first speaker, Professor Peng Geng-ke, who will deliver his keynote address titled, China’s Objectives and Actions in Building an Intelligent Society. Please join me in giving a warm round of applause to welcome Prof. Geng-ke to the stage. Thank you.

Gong Ke: Thank you so much for the introduction. And I will take this opportunity to introduce you briefly about the Chinese National Plan for Building an Intelligent Society. And you may already know that the Chinese government has released a top-tier plan for the new generation of artificial intelligence development from 2017 to 2030. It’s a long-term, high-level plan. And this plan is dubbed in China as a 1-2-3-4 planning. So one means to set up one national open and collaborative AI technological innovation system, one nationwide system. Two means to master the two attributes of artificial intelligence. One is its technical feature. Another one is the social feature of artificial intelligence. Three means three-in-one promotion. That means to advance the technical R&D, production manufacturing, and industrial nurturing in a three-in-one manner. The four means four aspects to be supported by AI development that are STI development, science technology innovation development, economic growth, social progress, and national security. So within this framework, the plan identifies six pivotal tasks with building an intelligent society, being a prominent one alongside with fostering technological innovation systems, nurturing an intelligent economy, and enhancing digital infrastructure. So the goals for building an intelligent society is that, in one word, it’s to build a safe and convenient intelligent society. And the plan outlines some objectives, some objectives. First is to accelerate the penetration of AI to elevate the equality of life and create an omnipresent intelligent environment, significantly augmenting the efficiency of social services and social management. The second is delegating simplistic, repetitive, and hazardous tasks to AI, thereby fostering human creativity and generating high-quality, comfortable employment opportunities. The third objective is to diversify and enrich on-demand intelligent services to maximize accessibility to high-quality social services and convenient lifestyle. The fourth objective is elevating the standards of intelligent social governance, rendering societal operations safer and more efficient. So under these goals and objectives, there are some tasks for building an intelligent society highlighted in the plan. The first is developing convenient and efficient intelligent services. That means prioritizing AI innovations to address urgent societal needs, such as education, especially pre-university education, health care, and elder care, to provide tailored, super-rare quality services. The second task is to advance intelligent social governance, leveraging AI to tackle administrative, judicial, municipal, and environmental governance challenges, thereby modernizing the social management of China. The third task is to enhance public safety through AI, promoting a profound application of AI in public safety, fostering the construction of an intelligent monitoring, early warning, and control system for public security. The fourth task is promoting trust and interaction in society to utilize AI to bolster social interactions and nurture trusted communication between the civic. To implement these tasks, China has established a policy framework for building an intelligent society. Based on the national plan, the Chinese government has issued various policies in the past years providing intricate pathways for intelligent society construction, which includes, first, science and technology innovation policies. supporting research endeavors and encouraging enterprises R&D investment and to establishment of a major national R&D project with joint public and private investment and we also have industrial development policies to cultivating industrial integration and innovation platforms guided by the document which is titled opinions on accelerating scenario innovation for promote high-quality AI applications which is issued by the Ministry of Industrial and Information Industry and we also have human resource development policies to fortifying talent cultivation frameworks and intensifying talent recruitment endeavors and now the AI courses is already adopted in the fundamental education and higher education systems in China and we have data and infrastructure policies enabling data sharing and the constructing intelligent infrastructure of course also privacy protection we have adopted a new law in China two years ago for protect the private information and then safety and the ethical norm policies establishing AI safety regulation and social impact evaluation system infused with ethical guidelines released by the Chinese government three years ago and finally regional development policies that is to encourage regional collaborations and localized development initiatives so with these policy frameworks China has kept I think notable progresses in building in intelligent society for example developing application scenarios in social services in health care China the artificially intelligent imaging screening technology has been dramatically improved early diagnostic diagnosis diagnosis or of critical units such as cancer in education intelligent systems has optimized the resource allocation fostering equity and equality so people can use AI aids AI assistance learning and teaching system in China widely in governance intelligent systems applied to pandemic control and public services having significantly enhanced efficiency in the campaign with pandemic and call it pandemic and we also achieved the progress in building intelligent public services system the widespread adoption of AI in transportation finance and environmental sectors have given rise to the convenient and efficient public service system so if you know the city Hangzhou is a very beautiful city near to Shanghai however there’s a big lake in the center of this city so that the transportation public transportation in this city is a very very difficult five years ago this city ranked number number three or number four the most suggestion traffic suggestion city in China with the help of AI system they reduce the ranking from number four to number 57 congestion city in China now so we have also enhanced the regulation and governance mechanism to ensure the safety use of AI and also China promoting green and intelligent synergy to leveraging AI to reduce the carbon print of social services and also production so in summary China’s endeavors in constructing a intelligent society are steadily transitioning from blueprint to reality so the blueprint is a national plan this transition not only mirrors technological advancement but also underscores a pathway to refining social governance and augmenting human well-being so in the future personally I believe China should further emphasize constructing an intelligent society centered on people rather than technology efforts should empower individuals with technology ensuring a dedicated balance between technological innovation and ethical operation ultimately the objective is to forge an inclusive equitable sustainable and harmonious society for all by use by leveraging the potential of artificial intelligence so that’s my brief introduction of China’s goals and actions in building a intelligent society thank you so much

Yuming Wei: thank you professor for your excellent presentation which provide a comprehensive overview of Chinese objectives plans and actions in building an intelligent society. And now let us welcome Mr. Sam Jules, who will deliver his speech on the topic of international governance of AI and the environment.

Sam Daws: Thank you very much. It’s a great pleasure to be here today with you all. I’ve only got 10 minutes, so I’m going to try and race through this quite promptly. First, what’s the positive contribution AI can make to climate solutions? Well, here’s just a few. New materials research in solar technologies, battery research, biodegradable alternatives to plastics, atmospheric modeling and climate modeling through digital twins. My colleague, Professor Philip Steer at Oxford University has a leading intelligent project on this. And AI will be vital to achieve the new UN climate COP energy efficiency goals across all industries. Well, what’s the problem? Well, AI energy and water consumptions are high and growing. This has global impact as a contributor to greenhouse gas emissions. Currently, AI accounts for one to 2% of global energy use, but it’s set potentially to increase significantly in the future. We need a new Japan worth of electricity every year because of AI, but also because of air conditioning and electric vehicles. And the water use of data centers uses more water than four times that of Denmark every year. Other factors that could increase AI’s energy use. There’s been an emphasis to date on the energy cost of testing and training large language models. But in the future, there’ll be greater emissions from inference, from multimodal search, and particularly important will be to track the energy use of semi-autonomous AI agents. And generative AI will shift not just from scraping the internet, but also relying on real-time IoT data of human behavior and natural processes involving greater data. So what are the solutions? Well, the UN environmental program in Nairobi, their recommendations are a good start. First, focus on the whole life cycle of AI, from the mining of critical materials all the way through to the deployment of AI models. Second, standardize the way we measure AI emissions. This work’s been taken forward by the ITU, the ISO, the IEC, IEEE, and others, including at the recent New Delhi Standard Summit. Next, incentivize transparency from industry on their energy use and emissions. Incentivize efficiency in hardware design, make software more efficient, such as the work of the Green Software Initiative. And data sobriety, more accurate, well-structured data, reducing duplication, only use the data necessary for the task. And lastly, powering new data centers only using renewable energy and reuse components at end of life. But can we leave energy optimization of data centers and chip design to industry alone? Well, perhaps, but that might change. I say perhaps because industry has achieved remarkable progress. Data center energy consumption only increased by 6% between 2010 and 2020, but computer workloads increased by 550%. NVIDIA achieved 100-fold increase in performance per watt from Kepler in 2012 to Hopper in 2023. Google achieved similar efficiencies through their TPUs. But despite these efficiencies, AI energy use and emissions overall continue to rise. So AI is therefore subject to the Jevons Paradox, named after an English economist who, back in 1865, observed that increased efficiency of coal use actually led to increased consumption of coal across a wide range of industries, and AI is following a similar path. So what are the prospects for a global interoperable approach on sustainable AI? Well, to have that, we need to navigate these geopolitical challenges. One, the greater US-China trade and national security competition. We’ve seen export controls, rare-earth export bans, and so on. Secondly, the new US administration is moving away from green regulation. Thirdly, sovereign AI trends may make it harder to shift testing, training, and influence to other countries. And fourthly, the new BRICS-AI alliance, announced this last week by President Putin, may lead to a bifurcation of policy approaches with the West. I want to end with what are the opportunities in 2025? Well, I think it’s vital we… use forward that includes China as well as the West. So we have the UN Universal Tracts that come out of the two UN General Assembly resolutions, Responsible AI, proposed by the US, co-sponsored by China, and AI Capacity Building, proposed by China, co-sponsored by the US. Great initiatives, we can have further cooperation. Then the UN Tracts that are emerging from the Global Digital Compact and the HLAB on AI, Science Convening, Policy Dialogue, Standards, Capacity Building, all can be used to advance sustainable AI. UNESCO’s Ethical Principles, ITU’s AI for Good Summit, and multi-stakeholder forums such as IGF. Then there’s national leadership. The Kingdom of Saudi Arabia’s leadership of the Digital Cooperation Organisation. They’re interested in an ethical framework for AI. Perhaps that could be a bridge involving China and the West. Malaysia’s chairing of ASEAN in 2025. They’ve got an interest in Responsible AI to double ASEAN’s digital economy. Singapore’s had leadership on greener data centres in humid tropical climates, in software, in integrating sustainability into its AI Verify and Model GNI frameworks. The EU and UK work on reducing digital emissions. The International Energy Agency’s report next spring on AI for Energy. France’s AI Action Summit, GPEI, the UK’s International Energy Security Summit, and Republic of Korea’s hosting of APEC economic leaders. These are all mini-lateral opportunities to further standardise approaches to measuring the energy cost of AI, but they can’t replace global initiatives that involve both China and the West. The last initiative, a really important one, is COP30 in Belém, Brazil. I wonder, can we have a higher ambition coalition on this issue of middle and smaller powers moving towards COP30? Possible national champions include Kenya, Singapore, the UAE, Saudi Arabia, Kazakhstan, Brazil, and France. That’s the end of my time. Thank you very much.

Yuming Wei: Thanks for Mr Strauss’ inspiteful speech. The energy and environment challenges in AI development are indeed global issues that require collaborative efforts from all countries around the world to address. Now, let’s welcome… who will be delivered on an online presentation from Beijing sub-forum. His speech is titled, 10 Epidemiological Questions on Generating Artificial Intelligence. Okay, Beijing, is there a voice clearly? Okay, let’s welcome. Hello. This is Feng Huizhang.

Min Jianing: This is from Beijing. Today, I’m going to talk about 10 Epidemiological Questions on Generating Artificial Intelligence. The rise of generative artificial intelligence triggered an unprecedented epidemiological revolution. This revolution is profoundly influencing human knowledge production, cognitive patterns, and social structures. To better grasp the full picture of this revolution and explore the future landscape of human-machine symbiosis, we must raise critical questions from an epidemiological dimension. So that’s why I want to read the 10 questions to cover various aspects of generative AI development, from a technological innovation to philosophical reflection, from social impact to value reshaping, systematically examining, and deeply exploring. These questions will provide us with important insights and action guides for understanding and responding to this revolution. Through researching the 10 questions, we can not only better grasp the development context and the trends of generative AI, but also more prudently consider the relationship between artificial intelligence and human society, contributing wisdom and strengths to achieving human-machine symbiosis and build a better future. So let’s take a look at the knowledge production and reshaping human nature, the revolution triggered by the generative AI and also the 10 questions. The question number one, what are the intrinsic mechanisms of evolution in knowledge production triggered by generative large-length model? Fundamentally, It is transforming the knowledge production mode, fundamentally changing our understanding of knowledge, truth, and cognition, shaking the epistemological presumptions of a subject, object, the autonomy and supremacy of reasons that have been established since the Enlightenment. 2. Does the emergence of generative large-language models signify the end of anthropocentrism, or does it make a new starting point for reshaping human nature? If intelligence is no longer the exclusive domain of humans, and creativity can also be simulated and surpassed by machines, then the status of a human as the spirit of all things will face unprecedented challenges. 3. Will the human-machine collaboration form a new paradigm for the knowledge exploration? When AI is not only a tool for human cognition, but also becomes a subject and even a partner in knowledge production, the relationship between humans and machines will inevitably undergo a profound restructuring. 4. How will generative large-language models subvert the traditional scientific research paradigm and open up new frontiers for knowledge discovery? The interaction between machine and human sciences to form amplified thinking will promote cross-disciplinary and integrated research humanized to disruptive innovations, and the Nobel Prize of Medicine and Physics have already shown that. And then I will talk about the social restructuring decision-making innovation, the transformation driven by generative AI, especially the decision-making innovation. So that is relevant with the question number five. Can generative large-language models help humans break through the limitations of funded rationality and achieve innovations in decision-making? With the help of the machine’s ability to extract insights from massive amounts of information, individuals will have the opportunity to transcend their own cognitive limitations and obtain more comprehensive and objective decision-making basis. About question number six, it will be relevant with the question of how will generative large-language models reshape the structure of human society and the relations of production. And the decision-making transformation is leading to the foundation of a traditional social division of labor because the non-differentiation of knowledge and skill acquisition triggered by large language model is based on the decisions. And then for question number seven, question number eight, how can we break down the disciplinary barriers and construct a fluid knowledge graph without disciplinary boundary? So that is exactly what question number seven is asking. Can generative large-language models break down disciplinary barriers and construct a fluid knowledge graph without disciplinary boundaries? The disciplinary classification system of individual error is built on the basis of a specialization and professionalization of knowledge production. And behind it lies the imprint of reductionism and mechanism. And question number eight, how will large language models revolutionize social science research and create a new paradigm with greater explanatory power, predictive power, and guiding power? With the help of large models, social sciences expect to establish an integrated research paradigm of data-driven human-machine collaboration and multi-scale linkage. So that is very interactive to the traditional way. The two last questions about redefinition of intelligence values, the philosophical reflections triggered by the generative AI. So for question number nine, how will the intelligence and creativity demonstrated by generative large language models redefine the human cognition? Because there is amazing creativity demonstrated by the large models based on the knowledge graph formed by training on massive corpora, blurs the boundaries between the imitation and innovation, quantitative and qualitative change. And question number 10, it is relevant to how the artificial intelligence is benchmarking towards the human intelligence. What are the connotations and extensions of aligning artificial intelligence with human values? What exactly it is aligning to? The question is not very clear yet. When the artificial intelligence system presents its difficult questions from unique perspectives, humans will be forced to re-examine existing beliefs and achieve value upgrades through openness, neutral learning, and evolution. So the human value system is also ever evolving. So that is the core of whether we should align with the peripheral part. Then, in summary, generative AI intelligence is pushing humanity towards a brand new era. In this era, the speed of knowledge iteration and updating will be greatly accelerated, and human-machine collaboration will promote the flourishing of science. Machine intelligence will help perfect social governance. The human-machine interaction will enhance human insight. When the artificial intelligence becomes the norm, the singularity will no longer be out of reach. In this new era, humanity will bid farewell to a civilization centered on individual intelligence and usher in a new era characterized by collective intelligence. Everyone will have their own personalized AI assistant, achieving the self-transcendence through the human-machine symbiosis. Facing this epistemological revolution led by generative AI, we should embrace the technology transformation with an open, prudent, and responsible attitude. And that’s why I put forward the 10 questions. Thank you.

Yuming Wei: Okay, thank you, Professor Mi Jianing, for sharing your thought-provoking perspective. I believe everyone has now gained a deeper understanding of the impact that generative AI will have on the cognitive system. Next, I welcome Professor Kevin D’Souza, who will deliver his speech titled Governing Cognitive Computer Systems for Public Value.

Kevin C. Desuoza: It’s okay, while the slides are being loaded. So it’s a pleasure to be here and address all of you. I would like to express my gratitude to the organizers of the event. Just two quick points. While I will present the presentation, I have a large group that helps me on a number of these projects. And so this is not just my work, it’s the work of my research group. the credit should go to them and these are just my views and they don’t officially reflect any group that we collaborate with. I guess we may not have slides. … … … … … … … … … … … … … Okay, and so one of the things that I thought that I would focus on is to broaden the discussion around AI. As you see in this image, AI is just a small piece of this larger revolution that’s underway right now on what we call cognitive computing systems. If you look at everything else around here you will notice three things. Number one, AI is probably the most developed field among the collection here. So if you look at things like neuropsychology, this is an emerging field. this is where we still have a lot of blue ocean. Whereas AI has been around for decades. The reason why I’m showing you this image is number one, it’s very important to put AI in the larger context if we want to talk about building transformative societies. AI will have a role to play, but it’s not the major role that it will play. It will work with a large assemblage of other innovations and other developments. Now, if you see everything on this image, you will notice one other thing. We are working at high speed when it comes to technical innovations in all of these areas. Yet, our governance and our frameworks to actually regulate and do responsible innovation have large amounts of inertia. And so, what I will do in the remaining few minutes is to highlight a few key points that hopefully will stimulate some further reflection on your part. So, if you can go to the next slide. Perfect. So, if you look at the other view of cognitive computing systems, you will see an image that looks like this. When we look at what really drives public value, we are trying to navigate these two issues of managing, governing actual behavior with cognitive computing systems and trying to understand what are individuals’ behavioral intentions. So, if you look at behavioral intentions, you will see things like risk. You will see things like privacy. When you look at actual intentions, you will see things like trust. You will see things like social presence. These are the areas where, again, work is underway. However, a lot of this work is. fairly disconnected from work going on that I showed you previously. Okay, so if you want to go to the next slide. So in the interest of time, I will not go through each of this. I will just highlight one thing at the end. So if you look at transparency, and it’s an issue that’s plaguing a lot of governments, our research has found that transparency is a very nuanced concept. There is transparency in terms of how government achieves a given outcome. There’s transparency in how we use technologies. And then there’s transparency in how government uses AI technologies. These three have different implications when it comes to explainable AI and our social licenses when it comes to innovating. Because I know we have two other speakers, I’ll go to the next slide. So one of the other areas, if we really want to build a truly global and AI or cognitive computing system driven society, we have to undertake fundamental work in terms of how interdependent our information platforms are, how interdependent our digital algorithms are. Because as recent examples have shown, if we have a single point of failure and it cascades around the ecosystem, we have actually increased the fragility of our societies. We haven’t increased it. Next slide. So the other issue that we have to do if we really want to uncover how do we get public value out of this stuff, is we have to begin tracking where is the money going. We have a long standing project where we’ve been looking at where governments around the world have been allocating their resources when it comes to advancement of AI. And so if you go to the next slide. And with these three, I’ll just highlight one thing. point each. Right now, a lot of the attention is on AI and large language models. To me, the technology is already out of the gate. It’s very hard to regulate. It’s very hard to govern when technology reaches a given scale. But we do have an opportunity when it comes to things like quantum computing. We need to get ahead of the curve rather than try to do it like we’ve been doing with previous generations of technology. The reason I bring up the Indonesia example is we have a lot of countries around the world that have forgotten the classical hierarchy of needs. Many countries around the world are deploying large language models for the higher levels of Maslow’s hierarchy of needs when they haven’t yet protected their databases. They haven’t yet prevented cyber attacks. So it’s this constant battle. And then lastly, one of the points we make in this report that’s coming out is, in order to truly reap the value of cognitive computing systems, we need to rethink how we design problems. So a very simple example. We are still trying to solve for health care in most countries, whereas the leading technology companies are solving for healthiness. They have completely flipped how they look at investments in health care. They are no more trying to solve for health care. They are trying to build healthier individuals. But for governments to be able to do that, they have to restructure government departments. They have to restructure ecosystems. And if we don’t do any of that, I believe we will never truly realize the value of these cognitive computing tools to make our societies more robust and innovative. Thank you.

Yuming Wei: Thanks for Professor Gisosa’s enlightening presentation. Your analysis of the public value of cognitive computer systems has provided us a new perspective for understanding AI and build stronger human-machine trust. Now let us welcome Professor Ru Peng from School of Public Policy and Management, Tsinghua University, who will deliver an online presentation from the Beijing Sub-Forum. Today’s topic is Governors’ Transformation and Standardization Development of the Intelligent Society. Please.

Ru Peng: Ladies and gentlemen, friends from Riyadh and Beijing, both online and offline, good afternoon. At present, the human society is moving towards an intelligent society, and a new generation of information technology represented by artificial intelligence is bringing significant and far-reaching impact to global economic growth, social development, and people’s lives. Chinese AI has formed a development trend of in-depth technological research and development, huge industrial scale, and diverse application scenarios. It can provide practical experience, leading demonstrations, and application feedback from the frontline for the development and governance of the global intelligent society, and provide exploratory and cutting-edge contributions. In order to use long-term cross-disciplinary and multidisciplinary empirical methods to record, describe, and predict the ongoing or upcoming changes in the intelligent society, under the leadership of Professor Su Jun, the Dean of the Institute for Intelligent Social Governance at Tsinghua University, I launched the initiative of conducting artificial intelligence social experiments and exploring the path of intelligent social governance in 2019 in collaboration with domestic and foreign experts and scholars, and promoted relevant appointments to build 92 national intelligent social governance experimental bases in 22 provinces across the country. To our knowledge, this is the largest social experiment on AI technology. and its governance on a global scale. After five years of practice, the experimental governance has achieved many important results and is continuously providing ideas, theories, and the technical standards and norms for building an intelligent society with human touch. For example, the city of Erdos in northern China has built the Duoduo Ping digital community service platform by using small QR codes to cover livelihood service and commercial operations enhancing the enthusiasm of the public to participate in community affairs in Erdos. The Hong Kong system and large-scale mining AI-based models have ensured the safety, greenness, and efficacy of coal production, promoting the intelligent transformation of the regional energy sector. For example, in the field of digital governance, China Mobile has provided an intelligent customer service experience and over 100,000 online Q&A services to 31 million people through the government affairs large model with a positive review rate of 98.7%, creating the most attentive intelligent government assistant. Based on the vivid practice on the vast land of China, we observed that the technical characteristics of AI are triggering a paradigm shift in its governance model. The humanoid nature, self-learning, adaptability, human-computer interaction, and the wide-ranging social impact of generative AI technology has led to a triple change in our AI governance. Number one, the transformation of our governance object from a material technological subject to a human-like technological subject and from static and stable technology to a dynamic and self-evolving technology. This requires us to pay close attention to issues such as the values, responsibility mechanism, and copyright mechanism of the big model and must adopt a flexible, open, and agile governance framework. Secondly, the governance interface has shifted from only dealing with the relationship between technical elements to take into account of the human-machine interaction. This calls for strengthened governance of issues such as information co-cons, cognitive bias, emotional manipulation, and addiction. Thirdly, the scope of governance has shifted from solely focusing on the process of technology co-innovation to emphasizing the micro-system of technology society policy involving multiple aspects such as ethics, social risks, and social impacts. This requires us to pay attention to the social applicability of technology and promote responsible development of AI. In facing with this shift in governance paradigms, we believe that standardization is the first move to address the opportunities and challenges of the times and build intelligent social culture and civilization. Standardization is not only a political tool with technical attributes but also strategic, leading, social, and people-oriented. In recent years, the clear trend of standardization internationally has been shifting from technical standards to governance standards, from standard refinement to standard prioritization. The main issues of standardization in AI have also expanded from traditional topics such as algorithms, data, and network security to comprehensive issues such as privacy, ethics, risk, management system, and social impact. In recent years, China has actively promoted the standardization of intelligent social governance. The relevant departments are studying and formulating the guidelines for standardization of intelligent social governance to build a standard system framework for intelligent social governance. In addition, the National Standardization Working Group on the Social Application and Evaluation of Intelligent Technology, SASWG35, which is headed by Tsinghua University as a secretariat, and I served as secretary general, has also conducted some useful explorations and has promoted the formal establishment of five national standards including social impact, generative AI, technology application, artificial intelligence, and social experiment. In the near future, we will continue to promote development of the key standards in areas such as social application for generative AI technology, smart healthcare, smart justice, and smart grassroots governance. Ladies and gentlemen, the future has arrived, the time is waiting for no one. We need to use standardized means to promote the healthy development of the intelligence technology, advance good governance of the intelligence society and serve the happy life of the people. We must adopt a prudent, positive and optimistic attitude to jointly address the risks and challenges brought by the intelligence technology. Let’s join hands and promote the development and governance of the intelligence society through the new paradigm of experimental governance, ensuring all countries and regions can benefit from the waves of the intelligence society and build a people-centered, humanistic intelligence society. Thank you.

Yuming Wei: Thank you, Professor Lupong, for your in-depth analysis of governance transformation and centralized path for intelligence society governance. Now let us welcome the final speaker, Mr. Pencilit Ilericic. As a highly experienced computer scientist, he will share his insights on leveraging information and communication technology as a tool for sustainable development. Because of the flight delay, Mr. Ilericic will speak online.

Poncelet Ileleji: Thank you very much. Mr. Ilericic, can you hear me? Yes, can you hear me? Can you hear me? Okay. Good morning. Good afternoon. Thank you all. It’s a great pleasure to be in this session. I just want to say that all the previous speakers that have spoken have basically addressed most of the issues that our collab loves to address. And I would like to start by saying the basic principles of how we use information computation technology, I’m talking mainly on artificial intelligence. It has to be human centered. And when it’s human centered, we are also dealing with issues that relate to trust and the respect of human values. Immediately we put that at the center of anything we do with artificial intelligence, be it with the various data models we collect or with the governance structure, then we have a good basis of discourse. And in talking about this, I would like us to go back to the final document of the government for AI for humanity, which was released in September, 2024 and by the UN AI advisory board. You should remember that this UN AI advisory board that was set up by the UN secretary general, Antonio Gutierrez in 2023, they are there on a volunteer basis independently. So their views do not reflect whatever organization or entity they refer to. And I want us to take, I would like to read from recommendation one, which I think is the basis of this session today. And one of the key recommendations from that document, which is recommendation one, was an international scientific panel of AI. And it was recommended that this panel has to be diverse and has to be multidisciplinary in terms of experts in various fields. And that is what this session has done. You know, we have had issues with quantum technology. We have had issues with using AI to mitigate climatic change, which is a big issue in the world today. But key things we should look at if we look very well at that recommendation one, we have to have annual reports in terms of surveying AI-related capabilities and opportunities and risk where there’s uncertainties. And this has to remain the core trust of what we do in terms of does it serve and respect human values? Is it really not encroaching on human rights? We also have to look at producing quarterly thematic research as that UN body says that will help AI, especially with achieving SDGs. Speaking as someone who comes from the Global South, we all know that in six years’ time, we’re going to be looking at the United Nations Substantive Development Goals. And if we use AI in whatever we do to try to achieve no to poverty or health or agriculture or climatic change, by emphasizing on the UN Substantive Development Goals 17, which deals with partnerships and cooperation, we’ll be able to achieve all we talked about here today. So I would like, colleagues, for us to reflect on the human-centric side of AI in what we do, especially with our young people, who are the ones going to be using this technology in everything they do. And they are the biggest social changes. Our governments have to understand this. Our companies have to understand this. And we have to start making sure that evidential-based research of the positive impacts of AI can make in the world we live in today. Thank you very much.

Yuming Wei: Thanks for Mr. Eladji for your wonderful speech. I find that although the speakers did not coordinate in advance, I note that their topics are highly complementary. President Gong He outlined China’s objectives and actions in building an intelligent society, while Professor Ru Peng further explored the governor’s dimension in this context. Professor Mi Jiayin examined the experimental challenges posed by generative AI, while Professor Kevin D’Souza proposed a governor’s approach with goal-oriented focusing for the AI cognitive systems. Mr. Sam Jules highlighted the importance of global governance in addressing energy and environmental problems in AI development, and Mr. Pancelet Eladji showcased the other side of AI’s role in promoting sustainable development. Due to time constraints, we are unable to proceed with further discussion and interaction. I would like to extend my heartfelt thanks to all six speakers today for sharing their brilliant and thought-provoking perspectives. Ladies and gentlemen, the further is already here. Let us embrace the intelligent society together. Thank you all, and we are looking forward to seeing you the next year. Thank you. Thank you.

Agreement Points

Need for global collaboration in AI governance

speakers

Sam Daws

Gong Ke

Ru Peng

Poncelet Ileleji

arguments

International collaboration on sustainable AI development

China’s national plan and objectives for AI development

Governance transformation and standardization for intelligent society

AI’s role in achieving UN Sustainable Development Goals

summary

Multiple speakers emphasized the importance of international cooperation and standardization in AI governance to address global challenges and promote sustainable development.

Human-centered approach to AI development

speakers

Poncelet Ileleji

Kevin C. Desuoza

Min Jianing

arguments

Human-centered approach to AI development

Cognitive computing systems and public value

AI’s influence on knowledge production and human nature

summary

Speakers agreed on the importance of putting human values and ethics at the center of AI development, considering its impact on society and human nature.

Similar Viewpoints

Both speakers addressed the need for sustainable AI development, with Sam Daws focusing on environmental challenges and Gong Ke mentioning China’s plan for sustainable AI growth.

speakers

Sam Daws

Gong Ke

arguments

High energy and water consumption of AI systems

China’s national plan and objectives for AI development

Both speakers emphasized the importance of governance frameworks and standardization in AI development to ensure public value and address societal challenges.

speakers

Ru Peng

Kevin C. Desuoza

arguments

Standardization as a tool for AI governance

Cognitive computing systems and public value

Unexpected Consensus

Interdisciplinary approach to AI development and governance

speakers

Min Jianing

Kevin C. Desuoza

Ru Peng

arguments

AI’s influence on knowledge production and human nature

Cognitive computing systems and public value

Governance transformation and standardization for intelligent society

explanation

Despite coming from different backgrounds, these speakers all emphasized the need for an interdisciplinary approach to AI development and governance, considering technological, social, and ethical aspects.

Overall Assessment

Summary

The speakers generally agreed on the importance of global collaboration, human-centered approaches, and interdisciplinary perspectives in AI development and governance. There was also consensus on the need for standardization and addressing environmental challenges.

Consensus level

The level of consensus among the speakers was relatively high, with complementary perspectives on key issues. This suggests a growing recognition of the complex, multifaceted nature of AI governance and the need for collaborative, holistic approaches to address global challenges and opportunities in AI development.

Different Viewpoints

Approach to AI governance

speakers

Gong Ke

Sam Daws

Ru Peng

arguments

China’s national plan and objectives for AI development

International collaboration on sustainable AI development

Governance transformation and standardization for intelligent society

summary

While Gong Ke focused on China’s national plan for AI development, Sam Daws emphasized the need for international collaboration, and Ru Peng stressed the importance of standardization in AI governance. This indicates different approaches to AI governance at national, international, and standardization levels.

Unexpected Differences

Focus on energy consumption of AI

speakers

Sam Daws

Other speakers

arguments

High energy and water consumption of AI systems

Need for energy optimization and efficiency in AI

explanation

Sam Daws was the only speaker to extensively discuss the environmental impact and energy consumption of AI systems. This focus on the ecological aspects of AI development was unexpected given the broader discussion on AI governance and societal impact.

Overall Assessment

summary

The main areas of disagreement centered around the approach to AI governance, the focus of AI applications, and the consideration of AI’s environmental impact.

difference_level

The level of disagreement among the speakers was moderate. While there were different emphases and approaches, there was a general consensus on the importance of responsible AI development and its potential to address global challenges. These differences in perspective can be seen as complementary rather than conflicting, potentially enriching the overall discussion on AI governance and development.

Partial Agreements

Both speakers agreed on AI’s potential to address global challenges, but Sam Daws focused specifically on climate solutions, while Poncelet Ileleji emphasized a broader range of Sustainable Development Goals.

speakers

Sam Daws

Poncelet Ileleji

arguments

AI’s potential contributions to climate solutions

AI’s role in achieving UN Sustainable Development Goals

Similar Viewpoints

Both speakers addressed the need for sustainable AI development, with Sam Daws focusing on environmental challenges and Gong Ke mentioning China’s plan for sustainable AI growth.

speakers

Sam Daws

Gong Ke

arguments

High energy and water consumption of AI systems

China’s national plan and objectives for AI development

Both speakers emphasized the importance of governance frameworks and standardization in AI development to ensure public value and address societal challenges.

speakers

Ru Peng

Kevin C. Desuoza

arguments

Standardization as a tool for AI governance

Cognitive computing systems and public value

Key Takeaways

China has a comprehensive national plan for AI development focused on building an intelligent society, with objectives like improving social services, governance, and public safety

Global governance and international collaboration are crucial for addressing challenges like energy consumption and environmental impact of AI development

AI and cognitive computing systems are reshaping knowledge production, decision-making processes, and social structures, requiring new governance approaches

Standardization is seen as an important tool for governing AI development and its societal impacts

There is a need for human-centered, ethical approaches to AI that align with human values and contribute to sustainable development goals

Resolutions and Action Items

Promote standardization efforts for AI governance, particularly in China

Explore opportunities for international collaboration on sustainable AI development, especially leading up to COP30

Continue research and experimentation on AI social impacts through initiatives like China’s 92 national intelligent social governance experimental bases

Unresolved Issues

Specific mechanisms for global interoperable approaches to sustainable AI development

How to balance national interests and global collaboration in AI governance

Concrete steps to align AI development with human values and ethics across different cultural contexts

Methods to effectively measure and mitigate the energy and environmental impacts of AI systems

Suggested Compromises

Leveraging existing UN frameworks and multi-stakeholder forums to bridge differences between China, the West, and other regions on AI governance

Balancing the pursuit of AI advancement with responsible development practices that consider social impacts and sustainability

AI is therefore subject to the Jevons Paradox, named after an English economist who, back in 1865, observed that increased efficiency of coal use actually led to increased consumption of coal across a wide range of industries, and AI is following a similar path.

speaker

Sam Daws

reason

This comment introduces a counterintuitive economic principle to explain why AI energy use continues to rise despite efficiency gains. It challenges the assumption that technological efficiency automatically leads to reduced resource consumption.

impact

This insight shifted the discussion towards the need for more comprehensive approaches to managing AI’s environmental impact beyond just improving efficiency. It added complexity to the conversation about sustainable AI development.

When the artificial intelligence system presents its difficult questions from unique perspectives, humans will be forced to re-examine existing beliefs and achieve value upgrades through openness, neutral learning, and evolution.

speaker

Min Jianing

reason

This comment presents AI not just as a tool, but as an entity capable of challenging human thinking and values. It suggests a more symbiotic relationship between humans and AI in intellectual and ethical development.

impact

This perspective expanded the discussion beyond technical and governance issues to consider the philosophical and ethical implications of AI development. It prompted deeper reflection on the nature of human-AI interaction and co-evolution.

We are still trying to solve for health care in most countries, whereas the leading technology companies are solving for healthiness. They have completely flipped how they look at investments in health care.

speaker

Kevin C. Desuoza

reason

This comment highlights a fundamental shift in problem-framing that AI enables. It demonstrates how AI can lead to reimagining entire sectors and approaches to societal challenges.

impact

This insight broadened the conversation to consider how AI might transform not just processes, but entire paradigms of thinking about social issues. It encouraged participants to think more creatively about AI’s potential impacts across various domains.

Standardization is not only a political tool with technical attributes but also strategic, leading, social, and people-oriented.

speaker

Ru Peng

reason

This comment reframes standardization from a purely technical process to a multifaceted approach for shaping societal development. It emphasizes the broader implications of how we set standards for AI.

impact

This perspective shifted the discussion towards considering standardization as a key lever for responsible AI development and governance. It highlighted the importance of interdisciplinary approaches in AI policy-making.

Overall Assessment

These key comments collectively broadened the scope of the discussion from technical and governance issues to include economic, philosophical, ethical, and societal dimensions of AI development. They challenged participants to think more holistically about the implications of AI, considering both its potential benefits and risks across various domains. The comments also emphasized the need for interdisciplinary approaches and creative problem-solving in addressing the challenges posed by AI. Overall, these insights deepened the complexity of the conversation and encouraged a more nuanced understanding of how AI might shape future societies.

How can we standardize the way we measure AI emissions?

speaker

Sam Daws

explanation

Standardizing AI emissions measurement is crucial for accurately assessing and managing the environmental impact of AI technologies.

How can we incentivize transparency from industry on their energy use and emissions?

speaker

Sam Daws

explanation

Industry transparency is essential for understanding and addressing the true environmental costs of AI development and deployment.

Can we have a higher ambition coalition of middle and smaller powers moving towards COP30 to address AI sustainability issues?

speaker

Sam Daws

explanation

A coalition of nations could drive progress on sustainable AI development and implementation at a global level.

What are the intrinsic mechanisms of evolution in knowledge production triggered by generative large-language models?

speaker

Min Jianing

explanation

Understanding these mechanisms is crucial for grasping the fundamental changes in how knowledge is created and disseminated in the age of AI.

How will generative large-language models reshape the structure of human society and the relations of production?

speaker

Min Jianing

explanation

This question addresses the potential societal and economic impacts of AI, which are critical for preparing for future changes.

How can we break down disciplinary barriers and construct a fluid knowledge graph without disciplinary boundaries using generative large-language models?

speaker

Min Jianing

explanation

This research area could lead to more integrated and holistic approaches to knowledge and problem-solving across various fields.

How will large language models revolutionize social science research and create a new paradigm with greater explanatory power, predictive power, and guiding power?

speaker

Min Jianing

explanation

This question explores the potential for AI to transform research methodologies and enhance our understanding of social phenomena.

What are the connotations and extensions of aligning artificial intelligence with human values?

speaker

Min Jianing

explanation

This question is crucial for ensuring that AI development remains ethical and beneficial to humanity.

How can we rethink problem design to truly reap the value of cognitive computing systems?

speaker

Kevin C. Desuoza

explanation

Redesigning how we approach problems could unlock the full potential of AI and cognitive computing in solving complex issues.

How can we develop key standards in areas such as social application for generative AI technology, smart healthcare, smart justice, and smart grassroots governance?

speaker

Ru Peng

explanation

Developing these standards is crucial for ensuring responsible and effective implementation of AI across various sectors of society.