Iterated Amplification is an approach to AI alignment, spearheaded by Paul Christiano. In this setup, we build powerful, aligned ML systems through a process of initially building weak aligned AIs, and recursively using each new AI to build a slightly smarter and still aligned AI.

See also: Factored cognition.