Model registries, in the context of AI regulation, are centralized governance databases of AI models intended to track and monitor AI systems in real-world use. These registries typically mandate the submission of a new algorithm or AI model to a governmental body prior to public release. 

Such registries will usually require basic information about each model, such as their purpose or primary functions, their computational size, and features of their underlying algorithms. In certain cases, they may request more detailed information, such as the model’s performance under particular benchmarks, a description of potential risks or hazards that could be caused by the model, or even certification that they have passed safety assessments designed to prove that the model will not cause harm.

Model registries allow governmental bodies to keep track of the AI industry, providing an overview of key models currently available to the public. Such registries also function as a foundational tool for AI governance – enabling future legislation targeted at specific AI models. 

These registries adhere to the governance model of “models as a point of entry”, allowing governments to focus their regulations on individual AI models rather than regulating the entire corporation, access to compute resources, or creating targeted regulations for specific algorithmic use cases.

As these model registries are an emerging form of AI governance with no direct precedents, the requirements, methods of reporting, and thresholds vary wildly between implementations. Some registries may be publicly accessible, providing greater accountability and transparency, whereas others may be limited to regulatory use only (e.g. when model data contains sensitive or dangerous information). Some may enforce reporting of certain classes of AI algorithms (such as China), whereas others may only require leading AI models with high compute requirements (such as the US).