AI safety is a research and engineering field focused on ensuring that AI systems behave reliably, remain under meaningful human control, and do not cause harm—whether through honest mistakes, adversarial misuse, or the pursuit of objectives that diverge from human intentions as systems become more capable. It spans near-term concerns such as preventing harmful outputs and reducing Hallucination, as well as longer-horizon questions about how to maintain oversight of systems that may eventually surpass human performance across broad domains.

Think of it like the discipline of aviation safety. Planes are not made safe by assuming pilots and engineers are careless; they are made safe through layered redundancies, failure-mode analysis, incident reporting, and continuous improvement of standards—because the consequences of failure are severe and sometimes irreversible.

Practical AI safety work today includes red-teaming models to find failure modes before deployment, developing interpretability tools to understand why a model produced a given output, designing training procedures that reward honesty and refusal of dangerous requests (such as Constitutional AI and RLHF with safety constraints), and establishing evaluation benchmarks for dangerous capabilities such as bioweapon synthesis assistance or autonomous deception.

AI safety intersects with AI Alignment (which addresses goal specification), RLHF (a core technique for steering behavior), Hallucination (a reliability and honesty failure mode), AI Governance (the policy layer), and the capabilities of Foundation Models (more capable models raise higher stakes). Organizations including Anthropic, OpenAI, and Google DeepMind maintain dedicated safety research teams.

You will encounter AI safety in model system cards, pre-deployment evaluation reports, safety-focused research papers, and in policy discussions around frontier model regulation and compute thresholds.