A Computational Model of Commonsense Moral Decision Making
Name
1801.04346.pdf
Description
Submitted version
Size
2.27 MB
Format
Unknown
Checksum (MD5)
66268ccb75b953e7d3fc170943412ee3
Author(s)
Kim, Richard
Kleiman-Weiner, Max
Abeliuk, Andrés
Awad, Edmond
Dsouza, Sohan
Tenenbaum, Joshua B
Rahwan, Iyad
Date Issued
December 27, 2018
Publisher
ACM
Citation
Kim, Richard, Kleiman-Weiner, Max, Abeliuk, Andrés, Awad, Edmond, Dsouza, Sohan et al. 2018. "A Computational Model of Commonsense Moral Decision Making."
Version
Original manuscript
Abstract
© 2018 ACM. 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.
MIT Department
Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1145/3278721.3278770
