Regional Cooperation in the Era of Digital Transition with Special Focus on AI for Sustainable Development Goals

We all recognize the profound internationalization in both production and R&D. The conventional evolutionary path starts with a trade network, progresses to a production network, and culminates in a science and innovation network. Different core drivers characterize each stage. In the initial stage, the globalization of trade was driven primarily by raw materials and demand. In the second stage, labor costs and regulations took the forefront. And in the globalization of innovation, human capital and infrastructure became the main drivers. However, we are experiencing a great challenge and transition not only in the digital and sustainability realms but also in political and international relations. This presents considerable risk to the existing structures of international cooperation. 

Advanced technologies are experiencing rapid turbulence. Not only are superpowers, like the US and China establishing and announcing their critical technology lists, but middle powers, like Germany, Japan, and Korea, are also crafting strategies focused on technological sovereignty, dependence issues, and the like. It's evident that AI, semiconductors, quantum information technology, and batteries are the core technologies. The critical concern is technological sovereignty. Interestingly, security has become an important criterion in promoting and safeguarding technology, a trend observed even in countries like Korea. However, robust international collaborations continue between many countries. According to a publication in Nature, collaboration between the United States and China remains a core feature. Additionally, the majority of AI publications still feature cooperation between the US and China. Intriguingly, economic collaboration between China and the Five Eyes Alliance Countries is more active than between the US and the Five Eyes. What I aim to underscore is the continued need for active cooperation in AI, even amidst the rise of technological protectionism.

A prime example is the application of AI for Sustainable Development Goals (SDGs) during the COVID crisis and ongoing climate challenges. We believe AI for SDGs presents a promising avenue. It's widely known that there are 17 goals within SDGs, and preparations are underway for the next set of SDGs due by 2025. The challenges in achieving these goals arise not just from COVID and climate crisis, but also biodiversity concerns. Many of these issues can be addressed through AI applications. For example, machine learning can help decipher new biodiversity workflows. Why AI for SDGs is effective and safe? While many sources attribute it to technology being "dual-use," I find this label limiting since any technology's application largely depends on its usage. The true determinants are the intent behind using the technology and its governance structure. In that regard, I think AI for SDGs is safe because it operates in an open competition setting, not confined to pre-competition stages, and it fosters regional cooperation that is inherently prosocial. Emphasizing AI for SDGs from the perspective of regional cooperation is vital because regions are highly interwoven and mutually dependent. They face shared challenges like local trade, climate change, pollution, and more. 

Regional digital cooperation, including AI, faces several obstacles. First, is the increasing uncertainty due to geopolitical tensions. Many experts have already pointed out the security problems including cyber security and data protection, and more recently, research security. Second, disparate approaches to AI exist among governments. While China and Korea share similarities, variations between countries arise concerning origin, transparency, interpretability, ethics, and regulation. The third obstacle is the economic downturn, which encompasses inflation, high-interest rates, trade deficits, a declining birth rate, among other issues. Lastly, the COVID-19 pandemic remains one of the biggest obstacles.

The principles of regional cooperation in these two sectors are paramount. Several points stand out. First, cooperation should be mutually beneficial, fostering sustainable development, particularly in regions like East Asia and South Asia. This can be achieved by prioritizing issues such as climate change, pollution, biodiversity, and pandemics. Second, security assurance is vital. We require an Asian or regional version of the risk assessment framework, akin to national standards and R&D risk assessment. Additionally, it’s essential to enhancing transparency, build ethical guidelines, and promote governance collaboration. Third, a pivotal principle is fostering long-term stable cooperation. Emphasizing collaborations for public good or sustainable development using these technologies is crucial. Finally, from an experience-sharing standpoint, its vital to address social challenges from a regional perspective and prioritize community-serving initiatives.

There are specific recommendations for regional cooperation, particularly regarding AI for SDGs. First and foremost is the adoption of a mission-oriented project for innovation cooperation. By embracing this mission-oriented approach centered on addressing social challenges, we can collaboratively design plans, mobilize resources, tackle R&D issues, monitor progress, harmonize policy, and foster collaboration.

Second, as previously referenced, we can develop regional ethical guidelines for digital and AI technologies. Collaboratively, we can build a risk management framework and establish cyber-security protocols for these technologies. From both a technological and governance perspective, a cooperative framework can be formulated. Ethical guidelines can be defined through a comparative analysis of each country’s strategy, and we can further refine risk management frameworks and cybersecurity protocols, emphasizing their implementation, recommendations, and monitoring.

Third, I believe we can collaborate on establishing regional platforms for sharing resources and IPR to ensure sustainable use. When implementing AI for SDGs on the regional level, it would be beneficial to share our intellectual property for public and societal problem-solving purposes. We can also promote a unified code platform, and design safe architecture for this initiative.