Towards Theory-based Moral AI: Moral AI with Aggregating Models Based on
  Normative Ethical Theory
Masashi Takeshita Rzepka Rafal Kenji Araki
Moral AI has been studied in the fields of philosophy and artificial
intelligence. Although most existing studies are only theoretical, recent
developments in AI have made it increasingly necessary to implement AI with
morality. On the other hand, humans are under the moral uncertainty of not
knowing what is morally right. In this paper, we implement the Maximizing
Expected Choiceworthiness (MEC) algorithm, which aggregates outputs of models
based on three normative theories of normative ethics to generate the most
appropriate output. MEC is a method for making appropriate moral judgments
under moral uncertainty. Our experimental results suggest that the output of
MEC correlates to some extent with commonsense morality and that MEC can
produce equally or more appropriate output than existing methods.