  AI alignment aims to make AI systems behave in line with human intentions and
values. As AI systems grow more capable, so do risks from misalignment. To
provide a comprehensive and up-to-date overview of the alignment field, in this
survey, we delve into the core concepts, methodology, and practice of
alignment. First, we identify four principles as the key objectives of AI
alignment: Robustness, Interpretability, Controllability, and Ethicality
(RICE). Guided by these four principles, we outline the landscape of current
alignment research and decompose them into two key components: forward
alignment and backward alignment. The former aims to make AI systems aligned
via alignment training, while the latter aims to gain evidence about the
systems' alignment and govern them appropriately to avoid exacerbating
misalignment risks. On forward alignment, we discuss techniques for learning
from feedback and learning under distribution shift. On backward alignment, we
discuss assurance techniques and governance practices.
  We also release and continually update the website (www.alignmentsurvey.com)
which features tutorials, collections of papers, blog posts, and other
resources.
