Generative AI Governance: Shaping a Collective Global Future

The document analyzes the fragmented global landscape of AI governance, categorizing national approaches as risk-based, rules-based, principles-based, or outcomes-based. It highlights critical tensions regarding existential versus present harms and open- versus closed-source models. The paper advocates for a multistakeholder approach to international coordination and the adoption of compatible standards and flexible regulatory mechanisms, such as sandboxes. A significant portion of the analysis focuses on the Global South, detailing structural inequalities in infrastructure, data, talent, and institutional capacity that hinder equitable access and governance participation.

• National AI governance approaches are diverse and often hybrid, falling into four primary categories: risk-based (prioritizing potential harm), rules-based (detailed requirements), principles-based (fundamental guidelines), and outcomes-based (measurable results).
• There is a significant policy debate between prioritizing long-term existential risks, which some argue could prevent overregulation and address threats to humanity, and focusing on present harms that disproportionately affect marginalized populations.
• The debate over open-source versus closed-source AI centers on a trade-off between democratizing innovation and reducing bias versus protecting intellectual property and preventing malicious use of powerful technology.
• To prevent a fragmented global environment with high compliance costs, the paper recommends international coordination through multistakeholder approaches, the development of compatible international standards, and the use of agile regulatory mechanisms.
• The Global South faces severe structural inequalities that create power imbalances in AI, specifically regarding access to compute infrastructure, representation in training data, technical talent, and institutional capacity for policy development.
• Current AI governance is heavily concentrated in a few regions, with 46% of frameworks developed in China, the EU, and North America, compared to only 5.7% in Latin America and 2.4% in Africa.

APA

World Economic Forum (2024). Generative AI Governance: Shaping a Collective Global Future. https://www3.weforum.org/docs/WEF_Generative_AI_Governance_2024.pdf

Chicago

World Economic Forum. Generative AI Governance: Shaping a Collective Global Future. 2024. https://www3.weforum.org/docs/WEF_Generative_AI_Governance_2024.pdf.

Wikipedia

{{cite report |author=World Economic Forum |title=Generative AI Governance: Shaping a Collective Global Future |date=2024 |url=https://www3.weforum.org/docs/WEF_Generative_AI_Governance_2024.pdf |access-date=23 October 2026 |via=Climate Insights Directory}}

BibTeX

@techreport{worldeconomicforum2024generative, author = {{World Economic Forum}}, title = {{Generative AI Governance: Shaping a Collective Global Future}}, institution = {World Economic Forum}, year = {2024}, url = {https://www3.weforum.org/docs/WEF_Generative_AI_Governance_2024.pdf}, urldate = {2026-10-23}, note = {Indexed by Climate Insights Directory} }