  Artificially intelligent systems, given a set of non-trivial ethical rules to
follow, will inevitably be faced with scenarios which call into question the
scope of those rules. In such cases, human reasoners typically will engage in
interpretive reasoning, where interpretive arguments are used to support or
attack claims that some rule should be understood a certain way. Artificially
intelligent reasoners, however, currently lack the ability to carry out
human-like interpretive reasoning, and we argue that bridging this gulf is of
tremendous importance to human-centered AI. In order to better understand how
future artificial reasoners capable of human-like interpretive reasoning must
be developed, we have collected a dataset of ethical rules, scenarios designed
to invoke interpretive reasoning, and interpretations of those scenarios. We
perform a qualitative analysis of our dataset, and summarize our findings in
the form of practical recommendations.
