Introduction

While legislation often dominates AI governance debates, an equally powerful—and less visible—battle occurs in the technical standardization of AI systems. The institutions defining safety, transparency, alignment, and interoperability standards will deeply shape the global deployment of AI for years to come (OECD, 2024; UN High-level Advisory Body on AI, 2024). Yet current standard-setting processes remain dominated by a small number of technically and financially powerful actors, raising concerns of governance asymmetry.

The Centrality of Standards in AI Governance

Technical standards serve as the operational layer of governance: they translate abstract principles like safety, fairness, or accountability into concrete engineering specifications, audit metrics, and compliance protocols (ISO/IEC JTC 1/SC 42, 2024; Cihon et al., 2024).

Examples include:

• Model evaluation benchmarks for safety and alignment.
• Dataset documentation protocols (e.g., data sheets, model cards).
• Red-teaming methodologies for frontier model evaluations.
• Interoperability frameworks for cross-border deployment.

Once adopted, these standards often become global norms that heavily influence regulatory design, procurement decisions, and industrial competitiveness (GPAI, 2024).

The Current Standardization Landscape

International Standardization Bodies

Key organizations include:

• International Organization for Standardization (ISO)
• International Electrotechnical Commission (IEC)
• Institute of Electrical and Electronics Engineers (IEEE)
• International Telecommunication Union (ITU) (OECD, 2024)

These bodies are drafting numerous AI-specific standards addressing safety, risk management, robustness, and transparency (ISO/IEC JTC 1/SC 42, 2024).

Private Actors’ Role

Large AI firms—such as Google DeepMind, OpenAI, Anthropic, Microsoft, and Amazon—frequently participate directly in standards committees, shaping technical definitions and embedding their system architectures into global benchmarks (Cihon et al., 2024; GPAI, 2024).

Limited Representation of the Global South

Many developing nations lack the technical expertise, resources, or institutional capacity to meaningfully participate in these highly specialized negotiations (UNESCO, 2023). This exacerbates existing global inequalities in AI governance.

Governance Asymmetries in Standardization

The standardization process introduces four core asymmetries:

• Resource asymmetry: Participation requires sustained legal, technical, and diplomatic resources.
• Expertise asymmetry: Frontier knowledge remains concentrated in private labs that dominate both model development and standard-setting.
• Transparency deficits: Many standardization negotiations occur behind closed doors with limited public visibility.
• Path dependency: Once technical standards are adopted, they shape downstream legislation, procurement, and compliance markets, locking in early mover advantages (Cihon et al., 2024).

These asymmetries risk entrenching the interests of dominant private actors while limiting democratic oversight, regulatory independence, and participation from underrepresented nations.

The Need for Institutional Correctives

To prevent global AI standardization from becoming an instrument of private and geopolitical consolidation, governance reforms must prioritize:

• Transparency in standard-setting processes.
• Capacity-building for underrepresented countries and public-interest organizations.
• Separation of private interest dominance from safety-critical standardization bodies.
• Independent scientific access to model evaluations informing standards.

As the UN High-level Advisory Body on AI (2024) emphasizes, technical standards should not function as de facto governance tools controlled by a small set of powerful actors.

Conclusion

Standardization is emerging as one of the most consequential, yet least transparent, sites of global AI governance. Without deliberate institutional reforms, AI standards risk embedding private architectures into public governance frameworks, limiting democratic oversight and deepening global power asymmetries.

References

Cihon, P., Maas, M. M., & Kemp, L. (2024). Boundaries for frontier AI governance. Science, 384(6688), 33-35. https://doi.org/10.1126/science.adn2123

Global Partnership on AI (GPAI). (2024). Annual report 2024: Advancing responsible AI governance. Paris: GPAI Secretariat. Retrieved from https://gpai.ai/

ISO/IEC JTC 1/SC 42. (2024). Artificial intelligence standards portfolio: 2024 update. Geneva: International Organization for Standardization.

OECD. (2024). State of implementation of the OECD AI Principles 2024. Paris: OECD Publishing.

UN High-level Advisory Body on AI. (2024). Interim report: Governing AI for humanity. United Nations.

UNESCO. (2023). Global Forum on the Ethics of AI: 2023 report. Paris: United Nations Educational, Scientific and Cultural Organization.