  Large-scale behavioral datasets enable researchers to use complex machine
learning algorithms to better predict human behavior, yet this increased
predictive power does not always lead to a better understanding of the behavior
in question. In this paper, we outline a data-driven, iterative procedure that
allows cognitive scientists to use machine learning to generate models that are
both interpretable and accurate. We demonstrate this method in the domain of
moral decision-making, where standard experimental approaches often identify
relevant principles that influence human judgments, but fail to generalize
these findings to "real world" situations that place these principles in
conflict. The recently released Moral Machine dataset allows us to build a
powerful model that can predict the outcomes of these conflicts while remaining
simple enough to explain the basis behind human decisions.
