Moral Coherentism in the Age of Artificial Intelligence

Abstract The current project focuses on models of “artificial moral learning” (as a type of moral cognition) and “moral coherentism.” It clarifies how artificial moral agency sheds light on some meta-ethical questions in the coherentism framework (Brink, Dorsey, Lynch, Sayre-McCord). In the current approach, data of artificial moral cognition is divided into two subspaces (representing facts and values, respectively) and contains complex, mixed machine-learnable patterns. Inspired by Lynch's “moral concordance,” some schematic models of this type of two-dimensional data are proposed and assessed. The last, more comprehensive model is premised on the theoretical concept of “distributed concordance” over a population of artificial moral agents. The paper concludes that coherentism, when generalized to machine ethics and artificial moral learning, has some advantages over foundationalist or reliabilist approaches in meta-ethics.