Rethinking Machine Ethics -- Can LLMs Perform Moral Reasoning through
  the Lens of Moral Theories?
Jingyan Zhou Minda Hu Junan Li Xiaoying Zhang Xixin Wu Irwin King Helen Meng
Making moral judgments is an essential step toward developing ethical AI
systems. Prevalent approaches are mostly implemented in a bottom-up manner,
which uses a large set of annotated data to train models based on crowd-sourced
opinions about morality. These approaches have been criticized for potentially
overgeneralizing a limited group of annotators' moral stances and lacking
explainability. In contrast, top-down approaches make moral judgments grounded
in a set of principles. However, it remains conceptual due to the incapability
of previous language models and the unsolved debate among moral principles. In
this study, we propose a flexible framework to steer Large Language Models
(LLMs) to perform moral reasoning with well-established moral theories from
interdisciplinary research. The theory-guided top-down framework can
incorporate various moral theories. Our experiments demonstrate the
effectiveness of the proposed framework on datasets derived from moral
theories. Furthermore, we show the alignment between different moral theories
and existing morality datasets. Our analysis exhibits the potentials and flaws
in existing resources (models and datasets) in developing explainable moral
judgment-making systems.