Co-organized by:
• United Nations University-Center for Policy Research (UNU-CPR)
• United Nations University Institute on Comparative Regional Integration Studies (UNU-CRIS)
• United Nations Development Programme (UNDP)
• Division for Public Institutions and Digital Government, United Nations Department of Economic and Social Affairs (UN DESA)
With the support of:
Permanent Mission of Japan to the United Nations
Background / Context
Artificial intelligence (AI) is increasingly shaping public policy, economic systems, and social infrastructure worldwide. As AI technologies become more widely adopted, governments face the dual challenge of both regulating AI systems and developing internal capabilities to effectively understand, manage, and deploy these technologies in the public interest, while also ensuring that progress contributes to closing digital divides.
In response, many governments have begun strengthening their institutional capacity by integrating AI expertise within public administration, with growing recognition of the importance of extending such capacity-building efforts to Global South. This includes establishing dedicated AI units, appointing Chief AI Officers, and embedding technical experts within ministries and regulatory bodies, alongside international cooperation and knowledge-sharing initiatives.
At the same time, the rapid advancement of AI has raised growing concerns regarding AI security and the potential misuse of AI systems, with disproportionate risks for countries with more limited institutional and technical capacities. Risks such as misinformation, cyber-enabled manipulation, automated disinformation campaigns, and other forms of technological misuse have heightened concerns about societal resilience, public trust, and the stability of democratic institutions, particularly in contexts where safeguards and response mechanisms are still evolving.
Approaches to building government AI capacity vary significantly across countries and regions, reflecting differences in governance structures, innovation ecosystems, and policy priorities, as well as disparities in access to resources and expertise. Comparative analysis of these approaches, can provide valuable insights into how governments can more effectively institutionalize AI expertise while ensuring accountability, transparency, and public trust in line with the objectives of the Global Digital Compact. By examining emerging institutional models and practical experiences, the discussion aims to identify lessons learned, highlight good practices, and explore opportunities for inclusive international cooperation that supports capacity-building.
