International AI governance refers to the global frameworks, policies, and practices that guide the responsible and ethical development, deployment, and use of Artificial Intelligence (AI) across nations. Today, AI development is largely concentrated in the hands of major corporations like OpenAI, leading to problematic practices such as scraping vast amounts of internet data, compromising user privacy, and dominating specific fields. These trends raise significant concerns for everyday users.
When ChatGPT was initially released, it was free and powerful, accessible to all users. This innovation led many to shift from traditional tools like Google Search or professional consultations to relying on ChatGPT for everything from answering multiple-choice questions to conducting in-depth research. Over time, our dependence on large language models (LLMs) has deepened.
As AI moves into sensitive domains like healthcare and finance, decentralized models can enhance trust, participation, and equity. The paper “A Decentralized AI Perspective” by Nanda et al. (MIT Media Lab) proposes an architecture that shifts control from single entities to distributed networks. Rather than consolidating data on central servers, models can be trained across multiple nodes using techniques such as federated learning, encrypted computation, and zero-knowledge proofs. These allow data to remain within legal jurisdictions ensuring compliance with national privacy laws while still contributing to global model performance.
Moreover, the paper emphasizes the role of open standards and incentive mechanisms in aligning participation. This enables international bodies such as the UN or the G7 AI Code of Conduct group to design collaborative AI systems that are governed by transparent protocols, rather than controlled by individual platforms.
To build decentralized AI systems, the global community must invest in privacy-preserving analytics, anonymous yet verifiable contribution protocols, fair and scalable incentive structures, and robust user interfaces and standards. Such systems would transfer control from monopolistic entities to a distributed model, where individuals, institutions, and nations can participate equitably without sacrificing data ownership or sovereignty.
Many countries are hesitant to share sensitive data, particularly in domains like healthcare, due to concerns about surveillance and privacy breaches. However, decentralized AI systems can overcome these challenges through encrypted learning, enabling cross-border AI development and benchmarking without exposing raw data.
Hence, decentralized AI systems provides a roadmap toward trustworthy, inclusive, and enforceable international AI governance. By enabling technical protocols that respect privacy, sovereignty, and transparency, it transforms AI from a centralized asset into a collaborative global good. As we move forward, building cross-border AI systems that reflect democratic values will require not just political will but decentralized architectures to make it feasible.