A Computational Model of Commonsense Moral Decision Making
Abstract
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.
Cite
@article{arxiv.1801.04346,
title = {A Computational Model of Commonsense Moral Decision Making},
author = {Richard Kim and Max Kleiman-Weiner and Andres Abeliuk and Edmond Awad and Sohan Dsouza and Josh Tenenbaum and Iyad Rahwan},
journal= {arXiv preprint arXiv:1801.04346},
year = {2018}
}