Towards Theory-based Moral AI: Moral AI with Aggregating Models Based on Normative Ethical Theory

Autor:	Takeshita, Masashi, Rafal, Rzepka, Araki, Kenji
Rok vydání:	2023
Předmět:	Computer Science - Artificial Intelligence Computer Science - Computation and Language
Druh dokumentu:	Working Paper
Popis:	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. Comment: Accepted IJCAI 2023 Workshop of Ethics and Trust in Human-AI Collaboration: Socio-Technical Approaches (EthAIcs 2023)
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2306.11432 Zobrazit plný text záznamu View this record from Arxiv