Building AI systems that are safe, reliable, and aligned with human values from the ground up.
We train our models using Constitutional AI methods, allowing them to self-correct and align with safety guidelines without requiring extensive human oversight. This approach ensures consistent behavior even in novel situations.
Every model output is monitored for potential safety issues. We maintain real-time dashboards tracking harmful content detection, bias metrics, and edge case behavior to quickly identify and address problems.
Before deployment, our models undergo extensive adversarial testing by dedicated red teams who attempt to elicit harmful or undesired behavior. This helps us identify vulnerabilities before they reach users.
Understanding how models make decisions is crucial for safety. We invest heavily in interpretability research to visualize and understand neural network behavior, enabling us to predict and prevent issues.