The classifiers approach aims to improve user experience for non-dangerous tasks while effectively blocking harmful content.

We are committed to actually trying to solve the real problems associated with AI safety, including misuse and misalignment risks.

The classifiers can detect attempts to circumvent the system, which is crucial for maintaining security.

Using a more capable classifier after serving a response can help flag potentially harmful outputs and involve human reviewers for the most dangerous responses.

The output classifier monitors the model's responses in real-time and can block dangerous outputs that violate our safety values.

Constitutional classifiers are a promising development in AI monitoring, demonstrating that smaller models can effectively check larger ones.

Our safety track record is strong, with about 10% of the world using our systems regularly, despite differing views on AI safety.

With the constitutional classifiers, we achieved thousands of hours of robustness to Red Teaming, significantly improving our defenses against jailbreaks.

Classifiers are decoupled from the text generation model, allowing for easier updates without unintended consequences in other domains, which gives customers a lot of assurance that there are no major changes happening in general to the model.