{"id":1524,"date":"2026-03-05T15:01:41","date_gmt":"2026-03-05T15:01:41","guid":{"rendered":"https:\/\/simoninstitute.ch\/?p=1524"},"modified":"2026-03-05T16:39:42","modified_gmt":"2026-03-05T16:39:42","slug":"global-dialogue-briefing-on-capacity-building-for-ai-governance","status":"publish","type":"post","link":"https:\/\/simoninstitute.ch\/blog\/post\/global-dialogue-briefing-on-capacity-building-for-ai-governance","title":{"rendered":"Global Dialogue Briefing on Capacity Building for AI Governance"},"content":{"rendered":"\n

In the lead up to the Global Dialogue on AI Governance<\/a>, the Simon Institute for Longterm Governance is hosting a briefing series together with the Permanent Missions of Singapore, Kenya, and Norway. The second briefing has focused on capacity building for AI governance, corresponding to the pre-defined Global Dialogue topic<\/a> 4b (capacity gaps), with relevance to topics 4c (social, economic, cultural, and linguistic implications of AI) and 4d (interoperability of AI governance).<\/p>\n\n\n\n

The event featured welcome remarks from H.E. James Ndirangu Waweru (Kenya) and H.E. Tormod Cappelen Endresen (Norway). Next, Cecil Abungu (Cambridge), who is the coordinator of ILINA<\/a>, an African-led AI research program, presented on the needs and challenges of capacity building for AI governance. An open discussion in a fishbowl format concluded the event.<\/p>\n\n\n\n

The case for AI governance capacity building<\/h2>\n\n\n\n

a) AI testing as a potential capacity gap<\/h3>\n\n\n\n

There are many AI capacity building initiatives. In the multilateral system these range from the UN Global Fund for AI with a funding target of $3 billion<\/a> to the World Bank\u2019s Digital Development Partnership<\/a>, the ITU\u2019s Partner2Connect Digital Coalition<\/a> or the UNDP\u2019s AI Hub for Sustainable Development<\/a>. Bilateral and minilateral programs include the EU\u2019s Global Gateway<\/a>, China\u2019s Digital Silk Road and Capacity-Building Action Plan<\/a>, the UK-Canada AI for Development<\/a> program, the Swiss International Computation and AI Network<\/a>, or the American AI Exports Program<\/a>. The main focus of these initiatives is to build the infrastructure and resources to develop and deploy AI systems.<\/p>\n\n\n\n

However, with maybe the exception of Germany\u2019s support<\/a> for building national AI strategies, there are limited capacity building efforts to build the skills and infrastructure needed for AI governance. Specifically, the ability to evaluate AI systems and to conduct post-market monitoring of impacts is a critical enabler of well thought through AI governance. Evaluation<\/a> tests for capabilities (what an AI model is able to do, including dangerous actions), propensity (what it does by default when given choices) and control (whether guardrails can be bypassed by someone actively trying to misuse th AI model) \u2013 critical information to figure out whether and how to deploy given AI systems. Currently, only about a dozen countries have dedicated AI testing institutes. The International Network for Advanced AI Measurement, Evaluation and Science<\/a> has ten members.<\/p>\n\n\n\n

b) Different levels of state capacity are likely to persist<\/h3>\n\n\n\n

A recurring pattern<\/a> across different types of divides is that absolute access gaps can be closed over time. In contrast, relative capacity gaps are likely to persist as these are largely a consequence of broader economic inequality. As one speaker noted, if a country needs to choose between vaccines and building an advanced AI testing facility, it will likely choose the former. As such, it would be unrealistic to assume that every country will have the same amount of state capacity to test AI and govern its deployment. <\/p>\n\n\n\n

There are established methods from domains, such as medicine or civil aviation, for how to manage state capacity differentials for testing, including:<\/p>\n\n\n\n