arXiv Open Access 2023

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

Masashi Takeshita Rzepka Rafal Kenji Araki
Lihat Sumber

Abstrak

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.

Topik & Kata Kunci

Penulis (3)

M

Masashi Takeshita

R

Rzepka Rafal

K

Kenji Araki

Format Sitasi

Takeshita, M., Rafal, R., Araki, K. (2023). Towards Theory-based Moral AI: Moral AI with Aggregating Models Based on Normative Ethical Theory. https://arxiv.org/abs/2306.11432

Akses Cepat

Lihat di Sumber
Informasi Jurnal
Tahun Terbit
2023
Bahasa
en
Sumber Database
arXiv
Akses
Open Access ✓