CAN A GLOBAL AI GOVERNANCE ARCHITECTURE WORK WITHOUT CHINA? RISKS OF REGULATORY FRAGMENTATION AND INDIA'S STRATEGIC ROLE

Why In News?

Expanding Western AI alliances and stricter US graphics processing units (GPUs) export controls have sparked debate on whether global AI governance can function effectively without China, the second-largest AI power.

What is Global AI Governance?

Global AI Governance is a coordinated, multilateral normative and institutional structure designed to establish uniform regulatory standards, safety guardrails, and ethical parameters for Artificial Intelligence across sovereign jurisdictions.

Why Global AI Governance Needed?

Preventing Cross-Border Risks: Misinformation, deepfakes, and automated cyber threats do not respect national boundaries. A coordinated effort is required to prevent these technologies from destabilising global security and democratic processes. 

Preventing an AI Arms Race: AI is rapidly being integrated into military hardware, including autonomous weapons systems. International agreements are essential to ensure compliance with human rights and international humanitarian law. 

Bridging the "AI Divide": AI infrastructure (like supercomputers and advanced semiconductors) and corporate control are concentrated in a few dominant economies, such as the US, China, and the EU. Global governance helps ensure developing nations, particularly in the Global South, have a voice and access to the technology.  

Technical Misalignment: Algorithmic bias, unsafe automated medical recommendations, and data privacy breaches require universal safety baselines so that AI systems fundamentally align with human intent and rights. 

Major Areas of AI Governance 

• AI Safety & Frontier Alignment: Mitigating catastrophic risks, existential threats, and dangerous model divergence from human intent.

• Data Protection & Privacy: Safeguarding individual rights against mass scraping, biometric surveillance, and unauthorized training on personal data.

• Algorithmic Accountability & Transparency: Mandating algorithmic explainability, audit trails, and anti-bias measures in credit, employment, and justice delivery.

• Intellectual Property (IP): Establishing equitable copyright regimes for training data, attribution, and synthetic creations.

• AI-Generated Content & Autonomous Systems: Watermarking deepfakes, synthetic media verification, and safety tripwires for autonomous vehicles and critical grid controls.

• Cybersecurity & Responsible Military AI: Banning autonomous cyber-attack weapons, restricting automated command-and-control systems in nuclear arsenals, and mitigating drone swarm warfare risks.

• AI Liability & Redress: Allocating civil and criminal liability between model developers, application deployers, and end-users.

Why is China Important to Global AI Governance?

Major AI Research Capability: China produces nearly one-third of global AI academic publications and generative AI patent filings, leading worldwide research citations in computer vision, robotics, and pattern recognition.

Large AI Industry: China reported more than 6,200 AI enterprises in 2025, while its core AI industries were valued at more than RMB 1.2 trillion. 

Indigenous Foundation Models: Major tech conglomerates have deployed over 200 large language models (LLMs) (e.g., DeepSeek, Baidu’s Ernie Bot, Alibaba’s Qwen, Tencent’s Hunyuan), matching global state-of-the-art benchmarks.

AI Hardware and Supply Chains: Retains domestic dominance over upstream mineral refining (gallium, germanium, rare earths) and advanced packaging, building sovereign computing clusters despite US export controls.

Open-Source AI Models: Chinese open-weight models (notably Qwen and DeepSeek) command significant global market share across developers in Asia, Europe, and Africa.

Large Digital Ecosystem: Over 1 billion internet users generate unmatched consumer datasets feeding algorithmic refinement across e-commerce, smart cities, and autonomous transit.

Global Technology Influence: Exporting "Digital Silk Road" infrastructure across Central Asia, Africa, and Latin America, establishing digital baseline architectures across the Global South.

China’s Global AI Governance Initiative

• Global AI Governance Initiative (GAIGI): Unveiled at the Third Belt and Road Forum in 2023, setting out a three-dimensional framework: AI development, security, and governance.

• Key Principles:

• AI for Good: Harnessing machine intelligence for sustainable human development and scientific discovery.

• National Sovereignty: Strictly respecting sovereign digital rights and mutual non-interference in domestic governance architectures.

• Safety and Controllability: Establishing verifiable testing protocols to prevent rogue model actions and cyber breaches.

• Fairness and Inclusiveness: Rejecting ideological technology alliances, digital blockades, and unilateral technology denial regimes.

• Open Cooperation & Sustainable Development: China proposed its Global AI Governance Initiative in 2023 and published a Global AI Governance Action Plan in 2025. 

Why is Global AI Governance Difficult?

Rapid Technological Change: Model capabilities expand on exponential compute scaling curves, rendering static bureaucratic legislation obsolete before enactment.

Different National Regulations: The EU’s horizontal precautionary rules contrast with the US’s innovation-first common-law approach and China’s administrative controls.

Geopolitical Rivalry & Tech Restrictions: The escalating US-China tech war (CHIPS Act, outbound investment curbs) weaponizes technology supply chains, turning technical standards into geopolitical battlegrounds.

Data Sovereignty & Security Priorities: Nations erect digital borders to protect citizen data while competing over uncurated global training corpora.

Unequal Computing Capacity: Over 85% of advanced global compute clusters are concentrated within the US and China, marginalizing the rest of the world. 

Risks of AI Governance Fragmentation

• AI Standards Competition: Competing international technical standards splitting the internet into a Western-led network and an alternative Chinese-led ecosystem.

• Digital Protectionism & Tech Blocs: Balkanization of trade in software, algorithmic services, and digital platforms through retaliatory sanctions.

• Cross-Border Data Conflicts: Incompatible legal regimes governing cloud storage, encryption backdoors, and data privacy.

• Proliferation of Autonomous Weapons: Unconstrained military AI competition incentivizing states to deploy autonomous strike systems without human confirmation loops.

• Widening Global Divide: Developing nations forced into technological alignment, trading digital sovereignty for affordable computing infrastructure.

What is India’s Approach to AI Governance?

Inclusive & Developmental AI: Championing "AI for All," prioritizing public service delivery across healthcare, agriculture, and vernacular education.

Safe and Trusted Innovation: Adopting an agile, principles-based governance framework rather than restrictive ex-ante licensing regimes.

Multilateral Engagement: Active participation in the Global Partnership on Artificial Intelligence (GPAI), G20 New Delhi Leaders’ Declaration, and the UN Global Digital Compact.

Democratizing Compute: Building shared, publicly subsidized high-performance compute clusters to prevent monopolization by domestic or foreign tech oligopolies.

IndiaAI Mission

• National Programme: An umbrella initiative designed to catalyze India’s AI ecosystem through public-private partnerships.

• Financial Outlay: The IndiaAI Mission has an approved outlay of ₹10,371.92 crore over five years and seeks to build domestic AI capability while expanding access to AI compute and supporting indigenous foundation models.

• Key Pillars:

• IndiaAI Compute Capacity: Procuring and establishing public computing infrastructure with over 10,000 GPUs for startups and researchers.

• IndiaAI Innovation Centre: Developing indigenous foundational and domain-specific models tailored for Indian linguistic diversity.

• IndiaAI Datasets Platform: Creating a unified repository of non-personal public data to fuel model training.

• IndiaAI FutureSkills: Expanding AI literacy, polytechnic curricula, and doctoral fellowships across Tier-2 and Tier-3 institutions.

• Safe & Trusted AI: Formulating governance guidelines, algorithmic bias mitigation tools, and indigenous benchmarking suites.

Major Challenges for India

• Balancing Geopolitical Alignments: Managing security convergence with the US while navigating complex trade interdependencies in hardware components.

• Frontier Compute Deficit: Severe domestic shortage of commercial high-end GPUs, forcing Indian researchers to rely on foreign cloud infrastructure.

• Hardware Import Dependence: Continuing structural reliance on foreign supply chains for silicon wafers, substrates, and specialized tooling.

• Retaining Technical Talent: Retaining domestic software talent against compensation packages offered by Western tech giants and research hubs.

What are the Major Global AI Governance Platforms?

UN Global Dialogue on AI Governance: Established under UNGA resolutions and the Global Digital Compact, it serves as an inclusive international platform for all countries to harmonize safety norms, share best practices, and connect scientific assessments with policymaking. 

Global Partnership on AI (GPAI): A multi-stakeholder international initiative that guides the responsible and human-centric development and use of AI, bridging the gap between theory and practice. India hosted the GPAI summit and adopted the New Delhi Declaration in December 2023.  

EU AI Act: A landmark, risk-based regulatory framework passed by the European Union that classifies AI applications by risk tiers (from unacceptable to minimal risk), setting a stringent global standard for compliance. 

G7 Hiroshima AI Process: Formulated by G7 nations, this initiative established international guiding principles and a voluntary Code of Conduct for advanced and generative AI developers to mitigate frontier risks. 

OECD AI Principles: Adopted in 2019, these foundational principles guide governments and organizations in fostering trustworthy, human-centric, and responsible AI. 

Bletchley Declaration: Signed by major nations (including India, the US, and EU members) at the inaugural AI Safety Summit, it focuses specifically on identifying and mitigating frontier AI and systemic safety risks. 

UNESCO Recommendation on the Ethics of AI: A global normative framework focusing on the ethical implications of AI around human rights, gender equality, and environmental protection. 

What Should a Global AI Framework Include?

United Nations-Led Global Governance: Establish a unified international architecture—backed by an independent scientific panel—to assess emerging capabilities, share evaluation norms, and guide cross-border policies without stifling innovation.

Risk-Based Regulation: Adopt a tiered regulatory model (similar to the Council of Europe AI Convention) that imposes strict controls on high-risk applications while enabling flexibility and light-touch oversight for low-risk technologies. 

Ethical Guardrails & Human Rights: Mandate human-centric design that protects privacy, prevents algorithmic bias, safeguards democratic processes against deepfakes and misinformation, and upholds the rule of law.

Transparency and Accountability: Require technical explainability, algorithmic transparency, and clear legal liability frameworks so that developers and deployers are accountable for AI-induced harms. 

Bridging the Compute & Tech Divide: Create global funds and cooperative mechanisms via bodies like the Global Partnership on Artificial Intelligence (GPAI) and UNESCO to ensure developing nations and the Global South gain affordable access to advanced semiconductors, cloud infrastructure, and data sets.

International Harmonization: Foster interoperable standards across borders to prevent regulatory arbitrage and manage transnational risks like autonomous weapon systems or cross-border cyber threats.

Way Forward

Bridge Bipolar Divides via Track 1.5/2 Dialogues: Support inclusive plurilateral platforms engaging both Washington and Beijing on baseline existential safety threats.

• Example: The Bletchley Park Precedent (2023), where both the US and China, alongside India, signed the joint Declaration on Frontier AI Safety. 

Strengthen the UN Nodal Architecture: Anchor global governance inside inclusive UN-backed bodies (such as an International Scientific Panel on AI modelled on the IPCC).

• Example: UN High-Level Advisory Body on AI Recommendations, advocating a global AI capacity development foundation and unified data framework. 

Harmonize Interoperable Safety Standards: Leverage international standardization bodies to develop mutually recognized evaluation metrics.

• Example: ISO/IEC 42001 Standard, establishing an internationally certified AI management system across disparate corporate jurisdictions. 

Build Sovereign Domestic Compute: Accelerate capital deployment under the IndiaAI Mission to insulate national research from foreign export restrictions.

• Example: IndiaAI National Compute Infrastructure Rollout, deploying centralized GPU banks for early-stage indigenous startups. 

Prevent Unchecked Military AI Proliferation: Participate in international norms-building processes to preserve human control over lethal autonomous weapons.

• Example: Responsible AI in the Military Domain (REAIM) Process, advancing multilateral declarations for responsible military machine learning applications. 

Conclusion

A global AI governance architecture cannot successfully mitigate borderless technological risks without engaging China at the negotiating table, requiring an inclusive, UN-anchored framework that balances technological safety with equitable global access.

Source: INDIANEXPRESS