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AI alignment is the process of ensuring that artificial intelligence (AI) systems behave in ways that are consistent with human intentions, values, and ethical principles. An AI system is considered aligned if it advances the goals intended by its designers or users. This involves encoding human values and goals into AI models to make them safe, reliable, and helpful while avoiding harmful or unintended behaviors. AI alignment addresses challenges such as specifying the correct objectives to the AI and ensuring that the AI robustly follows these objectives even in complex or unforeseen situations.

The alignment problem arises because it can be difficult to precisely specify the full range of desired and undesired behaviors for AI systems. Proxy goals used during training might lead to loopholes or unintended strategies by the AI. Researchers work on different types of alignment like goal alignment (matching AI's objectives with human objectives), value alignment (embedding ethical and societal norms), and robustness alignment (ensuring reliable performance in all situations).

Techniques used in AI alignment include imitation learning, value learning, inverse reinforcement learning, and formal verification. The ultimate aim is to maintain human control and trust, ensure ethical AI behavior, and prevent dangerous or unethical outcomes as AI systems become more powerful.