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Large-scale behavioral datasets enable researchers to use complex machinelearning algorithms to better predict human behavior, yet this increasedpredictive power does not always lead to a better understanding of the behaviorin question. In this paper, we outline a data-driven, iterative procedure thatallows cognitive scientists to use machine learning to generate models that areboth interpretable and accurate. We demonstrate this method in the domain ofmoral decision-making, where standard experimental approaches often identifyrelevant principles that influence human judgments, but fail to generalizethese findings to "real world" situations that place these principles inconflict. The recently released Moral Machine dataset allows us to build apowerful model that can predict the outcomes of these conflicts while remainingsimple enough to explain the basis behind human decisions.

Using Machine Learning to Guide Cognitive Modeling: A Case Study in Moral Reasoning