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Human-centred mechanism design with Democratic AI

Building artificial intelligence (AI) that aligns with human values is an unsolved problem. Here we developed a human-in-the-loop research pipeline called Democratic AI, in which reinforcement learning is used to design a social mechanism that humans prefer by majority. A large group of humans playe...

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Autores principales: Koster, Raphael, Balaguer, Jan, Tacchetti, Andrea, Weinstein, Ari, Zhu, Tina, Hauser, Oliver, Williams, Duncan, Campbell-Gillingham, Lucy, Thacker, Phoebe, Botvinick, Matthew, Summerfield, Christopher
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9584820/
https://www.ncbi.nlm.nih.gov/pubmed/35789321
http://dx.doi.org/10.1038/s41562-022-01383-x
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author Koster, Raphael
Balaguer, Jan
Tacchetti, Andrea
Weinstein, Ari
Zhu, Tina
Hauser, Oliver
Williams, Duncan
Campbell-Gillingham, Lucy
Thacker, Phoebe
Botvinick, Matthew
Summerfield, Christopher
author_facet Koster, Raphael
Balaguer, Jan
Tacchetti, Andrea
Weinstein, Ari
Zhu, Tina
Hauser, Oliver
Williams, Duncan
Campbell-Gillingham, Lucy
Thacker, Phoebe
Botvinick, Matthew
Summerfield, Christopher
author_sort Koster, Raphael
collection PubMed
description Building artificial intelligence (AI) that aligns with human values is an unsolved problem. Here we developed a human-in-the-loop research pipeline called Democratic AI, in which reinforcement learning is used to design a social mechanism that humans prefer by majority. A large group of humans played an online investment game that involved deciding whether to keep a monetary endowment or to share it with others for collective benefit. Shared revenue was returned to players under two different redistribution mechanisms, one designed by the AI and the other by humans. The AI discovered a mechanism that redressed initial wealth imbalance, sanctioned free riders and successfully won the majority vote. By optimizing for human preferences, Democratic AI offers a proof of concept for value-aligned policy innovation.
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spelling pubmed-95848202022-10-22 Human-centred mechanism design with Democratic AI Koster, Raphael Balaguer, Jan Tacchetti, Andrea Weinstein, Ari Zhu, Tina Hauser, Oliver Williams, Duncan Campbell-Gillingham, Lucy Thacker, Phoebe Botvinick, Matthew Summerfield, Christopher Nat Hum Behav Article Building artificial intelligence (AI) that aligns with human values is an unsolved problem. Here we developed a human-in-the-loop research pipeline called Democratic AI, in which reinforcement learning is used to design a social mechanism that humans prefer by majority. A large group of humans played an online investment game that involved deciding whether to keep a monetary endowment or to share it with others for collective benefit. Shared revenue was returned to players under two different redistribution mechanisms, one designed by the AI and the other by humans. The AI discovered a mechanism that redressed initial wealth imbalance, sanctioned free riders and successfully won the majority vote. By optimizing for human preferences, Democratic AI offers a proof of concept for value-aligned policy innovation. Nature Publishing Group UK 2022-07-04 2022 /pmc/articles/PMC9584820/ /pubmed/35789321 http://dx.doi.org/10.1038/s41562-022-01383-x Text en © The Author(s) 2022, corrected publication 2022 https://creativecommons.org/licenses/by/4.0/Open Access This 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 license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license 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 license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Koster, Raphael
Balaguer, Jan
Tacchetti, Andrea
Weinstein, Ari
Zhu, Tina
Hauser, Oliver
Williams, Duncan
Campbell-Gillingham, Lucy
Thacker, Phoebe
Botvinick, Matthew
Summerfield, Christopher
Human-centred mechanism design with Democratic AI
title Human-centred mechanism design with Democratic AI
title_full Human-centred mechanism design with Democratic AI
title_fullStr Human-centred mechanism design with Democratic AI
title_full_unstemmed Human-centred mechanism design with Democratic AI
title_short Human-centred mechanism design with Democratic AI
title_sort human-centred mechanism design with democratic ai
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9584820/
https://www.ncbi.nlm.nih.gov/pubmed/35789321
http://dx.doi.org/10.1038/s41562-022-01383-x
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