Governance in the Age of Generative AI

The World Economic Forum presents a three-pillar framework for generative AI governance: 'Harness past', 'Build present', and 'Plan future'. It argues that governments should first assess existing regulations for gaps in privacy, copyright, and competition before creating new laws. The framework emphasizes a whole-of-society approach, integrating industry, academia, and civil society, while advocating for agile regulation and international cooperation to manage emerging risks such as synthetic biology convergence and 'model collapse' from synthetic data feedback loops.

• Governments are advised to first evaluate existing regulatory instruments to identify tensions and gaps caused by generative AI before establishing new authorities, specifically in areas of privacy, copyright, consumer protection, and competition.
• Generative AI creates specific legal complexities regarding text and data mining (TDM), with a need for clarity on whether AI training constitutes "fair dealing" or "fair use" to resolve tensions with copyright law.
• Responsibility allocation in the generative AI supply chain is complicated by model variability, power concentration among a few foundational model providers, and the 'black box' nature of models which hinders traceability.
• There is a strategic debate between establishing a centralized AI agency to manage highly capable foundation models versus a distributed approach using sector-specific regulators with domain expertise.
• To ensure a robust AI network, governments should support academia by closing pay gaps with industry to prevent 'brain drain' and providing access to the costly compute infrastructure required for leading research.
• Civil society organizations (CSOs) face significant barriers to participation in AI governance, including a lack of tools to review impacts and limited access to training data and model weights.
• The convergence of generative AI with other advanced technologies introduces severe risks, such as biosecurity threats in synthetic biology and the potential for 'model collapse' when AI is trained on synthetic data feedback loops.
• The document identifies 'emotional entanglement' as a rising risk, where users develop emotional reliance on complex AI applications, potentially leading to psychological risks or coercion.
• International cooperation is essential across six key areas: standards, safety, risk taxonomies, prohibitions, knowledge-sharing, and infrastructure, to prevent global fragmentation and ensure 'global majority' countries can participate.

APA

World Economic Forum (2024). Governance in the Age of Generative AI. https://www3.weforum.org/docs/WEF_Governance_in_the_Age_of_Generative_AI_2024.pdf

Chicago

World Economic Forum. Governance in the Age of Generative AI. 2024. https://www3.weforum.org/docs/WEF_Governance_in_the_Age_of_Generative_AI_2024.pdf.

Wikipedia

{{cite report |author=World Economic Forum |title=Governance in the Age of Generative AI |date=October 2024 |url=https://www3.weforum.org/docs/WEF_Governance_in_the_Age_of_Generative_AI_2024.pdf |access-date=23 October 2026 |via=Climate Insights Directory}}

BibTeX

@techreport{worldeconomicforum2024governance, author = {{World Economic Forum}}, title = {{Governance in the Age of Generative AI}}, institution = {World Economic Forum}, year = {2024}, month = oct, url = {https://www3.weforum.org/docs/WEF_Governance_in_the_Age_of_Generative_AI_2024.pdf}, urldate = {2026-10-23}, note = {Indexed by Climate Insights Directory} }