迈向基于理论的道德人工智能：基于规范性伦理理论的聚合模型的道德人工智能
Towards Theory-based Moral AI: Moral AI with Aggregating Models Based on Normative Ethical Theory
AI总结：
本文实现MEC算法，聚合三种规范性伦理理论模型的输出以在道德不确定性下做出道德判断，实验表明其输出与常识道德相关且优于现有方法。
摘要：
道德人工智能已在哲学和人工智能领域得到研究。尽管大多数现有研究仅是理论性的，但人工智能的最新发展使得实现具有道德的AI日益必要。另一方面，人类处于不知道什么在道德上正确的道德不确定性之中。在本文中，我们实现了最大化期望选择价值（MEC）算法，该算法聚合基于规范性伦理学的三种规范性理论的模型的输出，以生成最合适的输出。MEC是一种在道德不确定性下做出适当道德判断的方法。我们的实验结果表明，MEC的输出与常识道德在一定程度上相关，并且MEC能够产生与现有方法同等或更合适的输出。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
