Research Focus

1. Artificial Intelligence (AI) Ethics and Governance

Background and research questions

Rapid advances in AI, particularly large-scale, autonomous, and generative systems, have intensified concerns over ethical risks, loss of control, and the inadequacy of existing governance frameworks. As AI systems increasingly operate across domains, jurisdictions, and social contexts, traditional regulatory approaches struggle to address extreme risks, value conflicts, and distributed responsibility. This raises fundamental questions about how AI risks should be conceptualized, governed, and aligned with societal values under conditions of uncertainty and rapid technological change.

Core research areas

Extreme AI risks and safety governance

Research in this area examines the risks associated with high-impact, potentially uncontrollable AI systems, including the lack of human oversight and unintended autonomous behavior. It explores governance approaches for managing extreme risks, safety investment incentives, and the role of international scientific consensus in defining safety thresholds and red lines.

Ethical frameworks for generative AI

From both the philosophical perspectives of technology and ethics, this research analyzes the value-laden nature of generative AI systems and the distribution of ethical responsibility among multiple actors, including developers, deployers, users, and regulators. It develops typological frameworks for AI ethical risks to support structured ethical assessment and policy design.

Agile governance for AI systems

This research focuses on governance models that can adapt to rapidly evolving AI technologies. It investigates agile governance approaches that emphasize institutional flexibility, multi-stakeholder coordination, scenario-based governance, and layered regulatory mechanisms across various governance objects and contexts.

Research outputs

Research in this area has contributed to international academic and policy debates on AI safety and ethics, including peer-reviewed publications on extreme AI risks and structured analytical frameworks for AI ethical governance. These outputs inform both domestic and international discussions on responsible AI development.

2. International Governance of Artificial Intelligence (AI)

Background and research questions

Global power dynamics, uneven technological capacities, and divergent regulatory approaches increasingly shape AI development. The absence of a unified international governance framework has exacerbated challenges of inclusiveness, coordination, and effectiveness. Key questions include how international AI governance architectures are evolving, how capacity gaps across countries affect governance outcomes, and how governance effectiveness can be measured.

Core research areas

Global governance architectures and capacity building

This research tracks trends in international AI governance and analyzes emerging governance architectures, with a focus on AI capacity-building as a foundation for inclusive and equitable global governance. It examines how different governance models interact with national interests, sovereignty concerns, and rule-making.

Open-source ecosystems and international standards

This research investigates the governance implications of open-source AI ecosystems and international standard-setting processes. It explores how open and collaborative development models shape technological diffusion, interoperability, transparency, and accountability across borders. The research further examines the role of standards in supporting responsible innovation, stakeholder coordination, and the sustainable development of AI systems globally.

Measurable governance and indicator systems

To address gaps between technological development and governance capacity, this research proposes the concept of measurable governance. It explores the construction of quantitative and evaluative indicators to assess governance effectiveness, reduce information asymmetries, and support evidence-based international cooperation.

Research outputs

This research has produced theoretical frameworks for understanding international AI governance and contributed to national-level and international policy research projects on global AI governance trends, capacity building, and governance effectiveness.

3. Governance of the AI Industry and Industrial Applications

Background and research questions

As AI technologies move from experimentation to large-scale deployment, their integration into industrial systems creates new governance challenges. These include uneven adoption across sectors, regulatory uncertainty, and the interaction between technological innovation and industrial transformation. Key research questions address how AI reshapes patterns of industrial innovation and how governance can support responsible, sustainable industrial AI development.

Core research areas

Empirical studies of AI industries and applications

This research is grounded in multi-level and cross-regional empirical studies of AI development in major innovation hubs. It focuses on key AI sectors and application scenarios, combining firm-level case studies with broader ecosystem analysis.

AI-driven industrial innovation dynamics

The research examines the co-evolution of AI technologies and industrial transformation, analyzing the simultaneous advancement of AI industrialization and industry-wide AI adoption. It investigates how AI-driven innovation aligns with established innovation lifecycle and diffusion models.

Autonomous driving as an AI-enabled industrial system

Autonomous driving is chosen as a representative AI-enabled industrial application that integrates advanced perception, decision-making, and control systems within complex socio-technical and regulatory environments. Research in this area focuses on governance challenges related to safety assurance, accountability, regulatory coordination, and public trust, as well as the role of standards and multi-stakeholder collaboration in supporting responsible deployment.

Research outputs

Results from this research provide systematic insights into AI-enabled industrial transformation and support evidence-based policy analysis. The research outcomes have informed analytical models and policy-oriented studies on AI industry development and governance.