Proceedings of the Annual Meeting of the Cognitive Science Society

Large-scale behavioral datasets enable researchers to use com-plex machine learning algorithms to better predict human be-havior, yet this increased predictive power does not always leadto a better understanding of the behavior in question. In thispaper, we outline a data-driven, iterative procedure that allowscognitive scientists to use machine learning to generate mod-els that are both interpretable and accurate. We demonstratethis method in the domain of moral decision-making, wherestandard experimental approaches often identify relevant prin-ciples that influence human judgments, but fail to generalizethese findings to “real world” situations that place these prin-ciples in conflict. The recently released Moral Machine datasetallows us to build a powerful model that can predict the out-comes of these conflicts while remaining simple enough to ex-plain the basis behind human decisions.