AI Governance Alliance Briefing Paper

The AI Governance Alliance (AIGA) presents a three-part series on generative AI. The first part introduces the Presidio AI Framework, which advocates for a 'shift-left' approach to risk mitigation by implementing technical and procedural guardrails early in the AI life cycle. The second part provides a roadmap for 'responsible transformation' in organizations, emphasizing use-case evaluation across business impact, operational readiness, and investment strategy, while managing workforce and sustainability impacts. The third part analyzes the fragmented global AI governance landscape, arguing for jurisdictional interoperability and the inclusive participation of the Global South to prevent power imbalances in the AI value chain.

• The Presidio AI Framework proposes a 'shift-left' methodology, which advocates for implementing safety guardrails at the earliest possible stage of the generative AI life cycle to increase efficiency and proactively manage risks.
• The Presidio AI Framework identifies four primary actors in the AI value chain: AI model creators, AI model adapters, AI model users, and AI application users, each with distinct responsibilities for safety and risk management.
• Generative AI model release is categorized on a gradient from 'fully closed' to 'fully open', where open-source models offer higher auditability and transparency but present a higher risk of malicious use and replication by bad actors.
• Organizations adopting generative AI are shifting from broad proofs of concept to smaller, use-case-based approaches that are evaluated through three specific 'gates': business impact, operational readiness, and investment strategy.
• Responsible organizational transformation requires a multistakeholder governance approach, including an AI ethics council based on value-based principles to manage downstream impacts on the workforce, sustainability, and the risk of hallucinations.
• Generative AI poses a specific risk to clerical jobs, which are traditionally held by women, potentially removing a historical entry point into the labor market, particularly in developing countries.
• Global AI governance is currently fragmented into four primary approaches: risk-based (e.g., EU AI Act), rules-based (e.g., China), principles-based (e.g., Canada), and outcomes-based (e.g., Japan).
• International jurisdictional interoperability is necessary to prevent the global AI environment from fracturing into non-interoperable spheres, which would create prohibitive compliance costs and complexity.
• There is a significant disparity in AI governance capacity, with 114 countries—almost exclusively from the Global South—lacking any national AI strategy as of a 2023 study.
• The Global South faces structural inequalities in AI development, specifically regarding access to compute, cloud infrastructure, high-quality education, and representation in training data, which are dominated by the US and China.

APA

World Economic Forum (2024). AI Governance Alliance Briefing Paper. https://www3.weforum.org/docs/WEF_AI_Governance_Alliance_Briefing_Paper_Series_2024.pdf

Chicago

World Economic Forum. AI Governance Alliance Briefing Paper. 2024. https://www3.weforum.org/docs/WEF_AI_Governance_Alliance_Briefing_Paper_Series_2024.pdf.

Wikipedia

{{cite report |author=World Economic Forum |title=AI Governance Alliance Briefing Paper |date=January 2024 |url=https://www3.weforum.org/docs/WEF_AI_Governance_Alliance_Briefing_Paper_Series_2024.pdf |access-date=23 October 2026 |via=Climate Insights Directory}}

BibTeX

@techreport{worldeconomicforum2024governance, author = {{World Economic Forum}}, title = {{AI Governance Alliance Briefing Paper}}, institution = {World Economic Forum}, year = {2024}, month = jan, url = {https://www3.weforum.org/docs/WEF_AI_Governance_Alliance_Briefing_Paper_Series_2024.pdf}, urldate = {2026-10-23}, note = {Indexed by Climate Insights Directory} }