Alignment

Alignment is the process of ensuring AI systems behave in accordance with human values, intentions, and ethical principles while avoiding harmful or unintended behaviors that conflict with human welfare. This multifaceted challenge involves training models to understand and follow human preferences, maintain helpfulness without causing harm, and operate within acceptable moral and ethical boundaries across diverse contexts. Alignment encompasses technical approaches such as reinforcement learning from human feedback (RLHF), constitutional AI, and value learning that optimize models toward desired behavioral patterns. The field addresses fundamental questions about AI safety, including how to specify human values computationally, prevent specification gaming, and ensure robust performance across novel situations. Advanced alignment research incorporates techniques like inverse reinforcement learning, cooperative inverse reinforcement learning, and scalable oversight to develop AI systems that remain beneficial as capabilities increase. Alignment represents a critical requirement for deploying powerful AI systems safely and maintaining human control over increasingly sophisticated artificial intelligence.