  This paper analyzes and compares 11 different proposals for building safe
advanced AI under the current machine learning paradigm, including major
contenders such as iterated amplification, AI safety via debate, and recursive
reward modeling. Each proposal is evaluated on the four components of outer
alignment, inner alignment, training competitiveness, and performance
competitiveness, of which the distinction between the latter two is introduced
in this paper. While prior literature has primarily focused on analyzing
individual proposals, or primarily focused on outer alignment at the expense of
inner alignment, this analysis seeks to take a comparative look at a wide range
of proposals including a comparative analysis across all four previously
mentioned components.
