Which AI governance model is defined as a mix between centralized and decentralized?

The hybrid model in AI governance refers to a framework that combines elements of both centralized and decentralized governance approaches. This model seeks to leverage the strengths of each method in order to create a more effective governance structure.

Centralized governance typically involves a single authority making decisions that affect the entire system, while decentralized governance distributes decision-making power across multiple nodes or entities. The hybrid model allows for flexibility, where certain aspects of AI governance can be managed centrally—such as overarching policy frameworks and compliance measures—while local entities or autonomous units retain the ability to make decisions that are more context-specific.

This combination ensures that while there is coherence and oversight from a central body, there is also the adaptability and responsiveness of decentralized entities to local needs and conditions. Such a model is particularly pertinent in AI governance where ethical considerations, data privacy, and compliance can vary widely across different jurisdictions and applications.

In contrast, the local governance model typically focuses on decisions made at a community or regional level, emphasizing autonomy and localized control. The cooperative model involves collaboration among various stakeholders to achieve shared goals but does not inherently combine centralization and decentralization. Finally, a distributed model emphasizes distributing control across multiple entities without a single point of authority, but it may not integrate centralized oversight. The