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Encoding Ethics to Compute Value-Aligned Norms

[journal article]

Serramia, Marc
Rodriguez-Soto, Manel
Lopez-Sanchez, Maite
Rodriguez-Aguilar, Juan A.
Bistaffa, Filippo
Boddington, Paula
Wooldridge, Michael
Ansotegui, Carlos

Abstract

Norms have been widely enacted in human and agent societies to regulate individuals' actions. However, although legislators may have ethics in mind when establishing norms, moral values are only sometimes explicitly considered. This paper advances the state of the art by providing a method for selec... view more

Norms have been widely enacted in human and agent societies to regulate individuals' actions. However, although legislators may have ethics in mind when establishing norms, moral values are only sometimes explicitly considered. This paper advances the state of the art by providing a method for selecting the norms to enact within a society that best aligns with the moral values of such a society. Our approach to aligning norms and values is grounded in the ethics literature. Specifically, from the literature's study of the relations between norms, actions, and values, we formally define how actions and values relate through the so-called value judgment function and how norms and values relate through the so-called norm promotion function. We show that both functions provide the means to compute value alignment for a set of norms. Moreover, we detail how to cast our decision-making problem as an optimisation problem: finding the norms that maximise value alignment. We also show how to solve our problem using off-the-shelf optimisation tools. Finally, we illustrate our approach with a specific case study on the European Value Study.... view less

Keywords
artificial intelligence; social norm; value; ethics; value judgement; optimization

Classification
Philosophy of Science, Theory of Science, Methodology, Ethics of the Social Sciences

Free Keywords
ethics and AI; moral values; decision support; norms; European Values Study 2017: Integrated Dataset (EVS 2017) (ZA7500 v4.0.0, doi:10.4232/1.13560)

Document language
English

Publication Year
2023

Page/Pages
p. 761-790

Journal
Minds and Machines, 33 (2023) 4

DOI
https://doi.org/10.1007/s11023-023-09649-7

ISSN
1572-8641

Status
Published Version; peer reviewed

Licence
Creative Commons - Attribution 4.0


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© 2007 - 2025 Social Science Open Access Repository (SSOAR).
Based on DSpace, Copyright (c) 2002-2022, DuraSpace. All rights reserved.