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

TL;DR

This paper introduces Scientific Regret Minimization (SRM), a new methodology for analyzing large datasets in psychology, demonstrated on moral decision data to uncover and validate novel moral phenomena.

Contribution

The paper proposes SRM, a systematic approach for building models and discovering phenomena in large datasets, improving upon traditional residual analysis methods.

Findings

Incorporating deontological principles improves moral judgment models.

Identified and validated three novel moral phenomena.

Demonstrated effectiveness on a dataset of 40 million moral decisions.

Abstract

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