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Artificial intelligence development races in heterogeneous settings

Regulation of advanced technologies such as Artificial Intelligence (AI) has become increasingly important, given the associated risks and apparent ethical issues. With the great benefits promised from being able to first supply such technologies, safety precautions and societal consequences might b...

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Autores principales: Cimpeanu, Theodor, Santos, Francisco C., Pereira, Luís Moniz, Lenaerts, Tom, Han, The Anh
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8810789/
https://www.ncbi.nlm.nih.gov/pubmed/35110627
http://dx.doi.org/10.1038/s41598-022-05729-3
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author Cimpeanu, Theodor
Santos, Francisco C.
Pereira, Luís Moniz
Lenaerts, Tom
Han, The Anh
author_facet Cimpeanu, Theodor
Santos, Francisco C.
Pereira, Luís Moniz
Lenaerts, Tom
Han, The Anh
author_sort Cimpeanu, Theodor
collection PubMed
description Regulation of advanced technologies such as Artificial Intelligence (AI) has become increasingly important, given the associated risks and apparent ethical issues. With the great benefits promised from being able to first supply such technologies, safety precautions and societal consequences might be ignored or shortchanged in exchange for speeding up the development, therefore engendering a racing narrative among the developers. Starting from a game-theoretical model describing an idealised technology race in a fully connected world of players, here we investigate how different interaction structures among race participants can alter collective choices and requirements for regulatory actions. Our findings indicate that, when participants portray a strong diversity in terms of connections and peer-influence (e.g., when scale-free networks shape interactions among parties), the conflicts that exist in homogeneous settings are significantly reduced, thereby lessening the need for regulatory actions. Furthermore, our results suggest that technology governance and regulation may profit from the world’s patent heterogeneity and inequality among firms and nations, so as to enable the design and implementation of meticulous interventions on a minority of participants, which is capable of influencing an entire population towards an ethical and sustainable use of advanced technologies.
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spelling pubmed-88107892022-02-03 Artificial intelligence development races in heterogeneous settings Cimpeanu, Theodor Santos, Francisco C. Pereira, Luís Moniz Lenaerts, Tom Han, The Anh Sci Rep Article Regulation of advanced technologies such as Artificial Intelligence (AI) has become increasingly important, given the associated risks and apparent ethical issues. With the great benefits promised from being able to first supply such technologies, safety precautions and societal consequences might be ignored or shortchanged in exchange for speeding up the development, therefore engendering a racing narrative among the developers. Starting from a game-theoretical model describing an idealised technology race in a fully connected world of players, here we investigate how different interaction structures among race participants can alter collective choices and requirements for regulatory actions. Our findings indicate that, when participants portray a strong diversity in terms of connections and peer-influence (e.g., when scale-free networks shape interactions among parties), the conflicts that exist in homogeneous settings are significantly reduced, thereby lessening the need for regulatory actions. Furthermore, our results suggest that technology governance and regulation may profit from the world’s patent heterogeneity and inequality among firms and nations, so as to enable the design and implementation of meticulous interventions on a minority of participants, which is capable of influencing an entire population towards an ethical and sustainable use of advanced technologies. Nature Publishing Group UK 2022-02-02 /pmc/articles/PMC8810789/ /pubmed/35110627 http://dx.doi.org/10.1038/s41598-022-05729-3 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Cimpeanu, Theodor
Santos, Francisco C.
Pereira, Luís Moniz
Lenaerts, Tom
Han, The Anh
Artificial intelligence development races in heterogeneous settings
title Artificial intelligence development races in heterogeneous settings
title_full Artificial intelligence development races in heterogeneous settings
title_fullStr Artificial intelligence development races in heterogeneous settings
title_full_unstemmed Artificial intelligence development races in heterogeneous settings
title_short Artificial intelligence development races in heterogeneous settings
title_sort artificial intelligence development races in heterogeneous settings
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8810789/
https://www.ncbi.nlm.nih.gov/pubmed/35110627
http://dx.doi.org/10.1038/s41598-022-05729-3
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