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Do large datasets provide value to psychologists? Without a systematicmethodology for working with such datasets, there is a valid concern thatanalyses will produce noise artifacts rather than true effects. In this paper,we offer a way to enable researchers to systematically build models andidentify novel phenomena in large datasets. One traditional approach is toanalyze the residuals of models---the biggest errors they make in predictingthe data---to discover what might be missing from those models. However, once adataset is sufficiently large, machine learning algorithms approximate the trueunderlying function better than the data, suggesting instead that thepredictions of these data-driven models should be used to guide model-building.We call this approach "Scientific Regret Minimization" (SRM) as it focuses onminimizing errors for cases that we know should have been predictable. Wedemonstrate this methodology on a subset of the Moral Machine dataset, a publiccollection of roughly forty million moral decisions. Using SRM, we found thatincorporating a set of deontological principles that capture dimensions alongwhich groups of agents can vary (e.g. sex and age) improves a computationalmodel of human moral judgment. Furthermore, we were able to identify andindependently validate three interesting moral phenomena: criminaldehumanization, age of responsibility, and asymmetric notions ofresponsibility.

Scaling up Psychology via Scientific Regret Minimization: A Case Study in Moral Decisions