AI Safety

The field asks how to make AI systems behave reliably, remain controllable, and avoid harms to people and society. Approaches include alignment training, red-teaming and benchmarks, interpretability, formal verification, runtime monitoring, and safety engineering for systems that use tools or act in the physical world. Researchers also study risks in deployment, including misuse, overreliance, organizational failures, and effects on vulnerable users, using methods from human-computer interaction, law, and governance. A central open tension is that strong results on bounded tests do not establish safety in long, changing real-world settings, while comprehensive guarantees for complex systems remain difficult to obtain.