  One of the most remarkable things about the human moral mind is its
flexibility. We can make moral judgments about cases we have never seen before.
We can decide that pre-established rules should be broken. We can invent novel
rules on the fly. Capturing this flexibility is one of the central challenges
in developing AI systems that can interpret and produce human-like moral
judgment. This paper details the results of a study of real-world decision
makers who judge whether it is acceptable to break a well-established norm:
``no cutting in line.'' We gather data on how human participants judge the
acceptability of line-cutting in a range of scenarios. Then, in order to
effectively embed these reasoning capabilities into a machine, we propose a
method for modeling them using a preference-based structure, which captures a
novel modification to standard ``dual process'' theories of moral judgment.
