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Humans feel too special for machines to score their morals

Artificial intelligence (AI) can be harnessed to create sophisticated social and moral scoring systems—enabling people and organizations to form judgments of others at scale. However, it also poses significant ethical challenges and is, subsequently, the subject of wide debate. As these technologies...

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Autores principales: Purcell, Zoe A, Bonnefon, Jean-François
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10266524/
https://www.ncbi.nlm.nih.gov/pubmed/37325024
http://dx.doi.org/10.1093/pnasnexus/pgad179
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author Purcell, Zoe A
Bonnefon, Jean-François
author_facet Purcell, Zoe A
Bonnefon, Jean-François
author_sort Purcell, Zoe A
collection PubMed
description Artificial intelligence (AI) can be harnessed to create sophisticated social and moral scoring systems—enabling people and organizations to form judgments of others at scale. However, it also poses significant ethical challenges and is, subsequently, the subject of wide debate. As these technologies are developed and governing bodies face regulatory decisions, it is crucial that we understand the attraction or resistance that people have for AI moral scoring. Across four experiments, we show that the acceptability of moral scoring by AI is related to expectations about the quality of those scores, but that expectations about quality are compromised by people's tendency to see themselves as morally peculiar. We demonstrate that people overestimate the peculiarity of their moral profile, believe that AI will neglect this peculiarity, and resist for this reason the introduction of moral scoring by AI.
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spelling pubmed-102665242023-06-15 Humans feel too special for machines to score their morals Purcell, Zoe A Bonnefon, Jean-François PNAS Nexus Social and Political Sciences Artificial intelligence (AI) can be harnessed to create sophisticated social and moral scoring systems—enabling people and organizations to form judgments of others at scale. However, it also poses significant ethical challenges and is, subsequently, the subject of wide debate. As these technologies are developed and governing bodies face regulatory decisions, it is crucial that we understand the attraction or resistance that people have for AI moral scoring. Across four experiments, we show that the acceptability of moral scoring by AI is related to expectations about the quality of those scores, but that expectations about quality are compromised by people's tendency to see themselves as morally peculiar. We demonstrate that people overestimate the peculiarity of their moral profile, believe that AI will neglect this peculiarity, and resist for this reason the introduction of moral scoring by AI. Oxford University Press 2023-05-29 /pmc/articles/PMC10266524/ /pubmed/37325024 http://dx.doi.org/10.1093/pnasnexus/pgad179 Text en © The Author(s) 2023. Published by Oxford University Press on behalf of National Academy of Sciences. https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Social and Political Sciences
Purcell, Zoe A
Bonnefon, Jean-François
Humans feel too special for machines to score their morals
title Humans feel too special for machines to score their morals
title_full Humans feel too special for machines to score their morals
title_fullStr Humans feel too special for machines to score their morals
title_full_unstemmed Humans feel too special for machines to score their morals
title_short Humans feel too special for machines to score their morals
title_sort humans feel too special for machines to score their morals
topic Social and Political Sciences
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10266524/
https://www.ncbi.nlm.nih.gov/pubmed/37325024
http://dx.doi.org/10.1093/pnasnexus/pgad179
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