  AI systems are becoming increasingly intertwined with human life. In order to
effectively collaborate with humans and ensure safety, AI systems need to be
able to understand, interpret and predict human moral judgments and decisions.
Human moral judgments are often guided by rules, but not always. A central
challenge for AI safety is capturing the flexibility of the human moral mind --
the ability to determine when a rule should be broken, especially in novel or
unusual situations. In this paper, we present a novel challenge set consisting
of rule-breaking question answering (RBQA) of cases that involve potentially
permissible rule-breaking -- inspired by recent moral psychology studies. Using
a state-of-the-art large language model (LLM) as a basis, we propose a novel
moral chain of thought (MORALCOT) prompting strategy that combines the
strengths of LLMs with theories of moral reasoning developed in cognitive
science to predict human moral judgments. MORALCOT outperforms seven existing
LLMs by 6.2% F1, suggesting that modeling human reasoning might be necessary to
capture the flexibility of the human moral mind. We also conduct a detailed
error analysis to suggest directions for future work to improve AI safety using
RBQA. Our data is open-sourced at
https://huggingface.co/datasets/feradauto/MoralExceptQA and code at
https://github.com/feradauto/MoralCoT
