A Computational Model of Commonsense Moral Decision Making

We introduce a computational model for building moral autonomous vehicles by learning and generalizing from human moral judgments. We draw on a cognitively inspired model of how people and young children learn moral theories from sparse and noisy data and integrate observations made from different people in different groups. The problem of moral learning for autonomous vehicles is cast as learning how to weigh the different features of the dilemma using utility calculus, with the goal of making these trade-offs reflect how people make them in a wide variety of moral dilemma. By modeling the structures of individuals and groups in a hierarchical Bayesian model, we show that an individual's moral values -- as well as a group's shared values -- can be inferred from sparse and noisy data. We evaluate our approach with data from the Moral Machine, a web application that collects human judgments on moral dilemmas involving autonomous vehicles, and show that the model rapidly and accurately infers people's preferences and can predict the difficulty of moral dilemmas from limited data.