With the rapid advancement of Artificial Intelligence (AI), the demand for diverse and high-quality data has surged, driving the need for effective data governance frameworks. At the same time, while societies become more and more digitalised, data is being generated at incredible speed. Recognizing these links, the UN Secretary-General’s High-Level Advisory Body on AI proposed that AI governance should be built in step with data governance and the promotion of data commons.

Currently, the majority of data is produced in a handful of languages and locations. Similarly, AI development is concentrated in a few countries and companies, while its governance remains fragmented, with limited representation from developing countries. These imbalances risk embedding biases that overlook the social, economic, environmental, and cultural contexts of underrepresented groups.

AI and data systems interact dynamically—more data enhance model training, increasing AI adoption and enabling further data collection and generation. This feedback loop, amplified by scaling effects and market dominance, risks widening technological and data divides, raising entry barriers for late adopters.

Objective

Ensuring AI and data contribute to development  requires multistakeholder cooperation to make both AI and data accessible, equitable, and beneficial for everyone. As emphasized in the Pact for the Future, strengthening international collaboration is crucial to harness the benefits of science, technology, and innovation (STI) while bridging the growing divide within and between countries.

This side event - co-organized by UNCTAD and ODET - will explore strategies to  enhance collaboration on infrastructure, data governance and skills to ensure AI drives inclusive and equitable development.