Brief

Regulating Cross-Border Data Flows: Harnessing Safe Data Sharing for Global and Inclusive Artificial Intelligence

This technology brief proposes a strategy to promote global cooperation to synergize conversations on cross-border data flow and AI.

In an increasingly interconnected world, data movement across international borders has become crucial to economic development, innovation and social advancement in an age of interconnected global networks. The international flow of data contributes to economic growth by fostering innovation, enhancing productivity, and facilitating international trade. However, calls to reduce barriers to cross-border data flows have sparked concerns regarding privacy, security, and data protection. The critical policy issue related to cross-border data flows is their potential restriction, particularly through data localization requirements. These requirements force organizations to restrict data access, sharing, and re-use within national borders. However, such restrictions can harm the functioning of markets and the prosperity of societies by limiting the benefits of sharing and re-using data across countries. Nevertheless, it is critical to proportionally address risks, consider the sensitivity of data and understand the purpose and context of processing.

Cross-border data flows are becoming increasingly important in the global artificial intelligence (AI) conversation. The ability to freely and securely transfer data across borders allows AI systems to access diverse information, which is an essential element of debiasing and democratizing AI. However, the emerging patchwork of regulatory approaches to data flows could hinder the deployment of AI systems globally, restrict access to data, and require the duplication of technologies and effort because of data location fragmentation. Therefore, to fully reap the benefits of AI, more interoperable regulatory approaches that enable the free flow of data with trust are needed.

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Regulating Cross-Border Data Flows: Harnessing Safe Data Sharing for Global and Inclusive Artificial Intelligence
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Suggested citation: Marwala Tshilidzi, Fournier-Tombs Eleonore and Stinckwich Serge. Regulating Cross-Border Data Flows: Harnessing Safe Data Sharing for Global and Inclusive Artificial Intelligence : UNU Centre, UNU-CPR, UNU Macau, 2023.

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