Deconfusing Corrigibility

Many thanks to Abram Demski for materially and intellectually supporting this research. Thanks also to Brian Berns, Roman Malov, Gurkenglas, and Lorxus for valuable conversations. Introduction Despite the central place of corrigibility to theoretical AI alignment, it seems like no one can really agree about what is . Much of this confusion is due to the fact that almost every definition of corrigibility proposed thus far is deficient in one way or another. In particular, the most prevalent existing accounts of corrigibility are over-epistemicized (modeling corrigibility as arising purely from uncertainty about the AI's goal, which gives rise to issues like the problem of fully updated deference) or evaluatively-thickened (making the AI's normative priorities instrumental to its corrigibility as a result of forcing corrigibility to be a single unified goal.) Here, my intention is to clear up some of these confusions and present a new picture of corrigibility that builds on these existing accounts to specify a goal that is more congruous with the original 2015 desiderata , and admits the kind of formal safety guarantees we need the concept to entail. I argue that the fundamental conc