  Value alignment is the task of creating autonomous systems whose values align
with those of humans. Past work has shown that stories are a potentially rich
source of information on human values; however, past work has been limited to
considering values in a binary sense. In this work, we explore the use of
machine learning models for the task of normative principle prediction on
naturally occurring story data. To do this, we extend a dataset that has been
previously used to train a binary normative classifier with annotations of
moral principles. We then use this dataset to train a variety of machine
learning models, evaluate these models and compare their results against humans
who were asked to perform the same task. We show that while individual
principles can be classified, the ambiguity of what "moral principles"
represent, poses a challenge for both human participants and autonomous systems
which are faced with the same task.
