AI Infrastructure Standardisation as a Public Good

As artificial intelligence (AI) systems become more integral to national productivity, service delivery, and innovation, the standardisation of AI infrastructure is emerging as a critical public good. This encompasses the development and adoption of interoperable, inclusive, and safe digital foundations that enable responsible AI deployment across sectors and borders.

Why Standardisation Matters

Without common standards, AI development risks fragmentation, duplication, and systemic exclusion. Just as electricity grids required standard voltage and plug types to scale equitably, AI infrastructure—data formats, APIs, model registries, compute protocols—requires standardisation to ensure broad, safe, and efficient adoption.

Standardised AI infrastructure:

1. Reduces barriers to entry for smaller actors and startups.
2. Encourages trust and accountability in AI systems.
3. Enables interoperability between institutions, countries, and platforms.
4. Facilitates regulatory oversight and ethical compliance.
5. Ensures equitable access to compute and data resources, especially in the Global South.

Public Good Characteristics

AI infrastructure standardisation qualifies as a public good under the traditional economic definition:

1. Non-excludable: Once established, no entity can be prevented from benefiting.
2. Non-rivalrous: One user’s access does not diminish another’s.

Examples include:

1. Open datasets aligned with common schema and licensing.
2. Model-sharing platforms with standard metadata.
3. Federated learning protocols for privacy-preserving AI collaboration.
4. Ethical and legal frameworks encoded as machine-readable standards.

Global and Local Opportunities

At the global level, initiatives like the OECD AI Principles, UNESCO’s AI Ethics Framework, and IEEE’s P7000 series lay the groundwork. However, local contexts matter. For instance, Africa’s AI landscape must adapt these standards to resource constraints, cultural contexts, and linguistic diversity.

Governments, multilaterals, and DPI-aligned platforms must invest in:

1. Public registries of models and datasets.
2. Open-source AI infrastructure stacks tailored to local needs.
3. Inclusive standard-setting bodies that reflect diverse geographies and sectors.
4. AI sandboxes for responsible experimentation and iteration.

Policy and Implementation Considerations

To institutionalise standardisation as a public good, policymakers should:

1. Embed AI standardisation into digital public infrastructure frameworks.
2. Fund open-source infrastructure and public-interest AI tooling.
3. Foster public-private-academic consortia to co-create standards.
4. Use public procurement to enforce adherence to standards.

What This Means for Policymakers

1. Invest in shared AI infrastructure with interoperability and ethical design baked in.
2. Collaborate across borders to shape global AI norms while localising them effectively.
3. Legislate to require minimum standards for high-impact AI applications.
4. Measure AI’s public value not just in GDP terms, but in terms of access, equity, and accountability.