In AI governance, which model allows authority at lower levels within an organization?

The decentralized model in AI governance is designed to empower lower levels within an organization by distributing authority and decision-making capabilities across various units or teams. This fosters a more collaborative environment where local managers or teams can tailor AI initiatives to their specific contexts, thereby enhancing responsiveness to local needs and challenges.

Moreover, a decentralized approach can lead to increased innovation, as teams are encouraged to experiment and adapt AI technologies without needing to seek approval from a central authority for every decision. This can be particularly beneficial in dynamic industries where agility and quick responses to emerging opportunities are essential for success.

In contrast, the centralized model consolidates authority within a single, top-level entity, which can lead to bottlenecks in decision-making and may stifle local initiative. The hybrid model typically combines elements of both centralized and decentralized approaches but does not fully enable lower-level authority in the way that a purely decentralized model does. The unified control model usually refers to a structure where all governance is tightly controlled from a single source, limiting local decision-making.