  We introduce a new computational model of moral decision making, drawing on a
recent theory of commonsense moral learning via social dynamics. Our model
describes moral dilemmas as a utility function that computes trade-offs in
values over abstract moral dimensions, which provide interpretable parameter
values when implemented in machine-led ethical decision-making. Moreover,
characterizing the social structures of individuals and groups as a
hierarchical Bayesian model, we show that a useful description of an
individual's moral values - as well as a group's shared values - can be
inferred from a limited amount of observed data. Finally, we apply and evaluate
our approach to data from the Moral Machine, a web application that collects
human judgments on moral dilemmas involving autonomous vehicles.
