Even well-intentioned systems fail. They encounter situations their designers didn’t anticipate. They are deployed in contexts far removed from their training environments. They interact with other systems in ways that produce unpredictable outcomes. AI safety research addresses how AI behaves under these conditions and how to build systems that remain reliable, correctable, and constrained in their impact when things go wrong.

This is the engineering discipline of responsible AI. Where alignment asks what a system is trying to do, safety asks whether it will do so reliably and without causing harm, especially in high-stakes environments like housing, employment, criminal justice, medical diagnosis, infrastructure management, and autonomous vehicles, where errors carry real consequences for real people.

The Lab’s safety work spans robustness testing (how systems perform under distribution shift and edge cases), corrigibility research (ensuring AI systems can be monitored, corrected, and shut down), and the development of evaluation benchmarks that practitioners and regulators can apply across sectors. Safety is the discipline that transforms alignment from a design intention into a deployable guarantee.