TY - GEN
T1 - Local Justice and Machine Learning
T2 - 37th AAAI Conference on Artificial Intelligence, AAAI 2023
AU - Chen, Violet
AU - Williams, Joshua
AU - Leben, Derek
AU - Heidari, Hoda
N1 - Publisher Copyright:
Copyright © 2023, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.
PY - 2023/6/27
Y1 - 2023/6/27
N2 - We consider a setting in which a social planner has to make a sequence of decisions to allocate scarce resources in a high-stakes domain. Our goal is to understand stakeholders' dynamic moral preferences toward such allocational policies. In particular, we evaluate the sensitivity of moral preferences to the history of allocations and their perceived future impact on various socially salient groups. We propose a mathematical model to capture and infer such dynamic moral preferences. We illustrate our model through small-scale human-subject experiments focused on the allocation of scarce medical resource distributions during a hypothetical viral epidemic. We observe that participants' preferences are indeed history- and impact-dependent. Additionally, our preliminary experimental results reveal intriguing patterns specific to medical resources-a topic that is particularly salient against the backdrop of the global covid-19 pandemic.
AB - We consider a setting in which a social planner has to make a sequence of decisions to allocate scarce resources in a high-stakes domain. Our goal is to understand stakeholders' dynamic moral preferences toward such allocational policies. In particular, we evaluate the sensitivity of moral preferences to the history of allocations and their perceived future impact on various socially salient groups. We propose a mathematical model to capture and infer such dynamic moral preferences. We illustrate our model through small-scale human-subject experiments focused on the allocation of scarce medical resource distributions during a hypothetical viral epidemic. We observe that participants' preferences are indeed history- and impact-dependent. Additionally, our preliminary experimental results reveal intriguing patterns specific to medical resources-a topic that is particularly salient against the backdrop of the global covid-19 pandemic.
UR - http://www.scopus.com/inward/record.url?scp=85167869122&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85167869122&partnerID=8YFLogxK
U2 - 10.1609/aaai.v37i5.25737
DO - 10.1609/aaai.v37i5.25737
M3 - Conference contribution
AN - SCOPUS:85167869122
T3 - Proceedings of the 37th AAAI Conference on Artificial Intelligence, AAAI 2023
SP - 5956
EP - 5964
BT - AAAI-23 Technical Tracks 5
A2 - Williams, Brian
A2 - Chen, Yiling
A2 - Neville, Jennifer
Y2 - 7 February 2023 through 14 February 2023
ER -