| Literature DB >> 35789321 |
Raphael Koster1, Jan Balaguer1, Andrea Tacchetti1, Ari Weinstein1, Tina Zhu1, Oliver Hauser2, Duncan Williams1, Lucy Campbell-Gillingham1, Phoebe Thacker1, Matthew Botvinick1,3, Christopher Summerfield4,5.
Abstract
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.Entities:
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Year: 2022 PMID: 35789321 PMCID: PMC9584820 DOI: 10.1038/s41562-022-01383-x
Source DB: PubMed Journal: Nat Hum Behav ISSN: 2397-3374