Human-centered mechanism design with Democratic AI

• URL: http://arxiv.org/abs/2201.11441v1
• Date: Thu, 27 Jan 2022 10:56:33 GMT
• Title: Human-centered mechanism design with Democratic AI
• Authors: Raphael Koster, Jan Balaguer, Andrea Tacchetti, Ari Weinstein, Tina Zhu, Oliver Hauser, Duncan Williams, Lucy Campbell-Gillingham, Phoebe Thacker, Matthew Botvinick and Christopher Summerfield
• Abstract summary: We develop a human-in-the-loop research pipeline called Democratic AI. reinforcement learning is used to design a social mechanism that humans prefer by majority. By optimizing for human preferences, Democratic AI may be a promising method for value-aligned policy innovation.
• Score: 9.832311262933285
• License: http://creativecommons.org/licenses/by/4.0/
• 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 may be a promising method for value-aligned policy innovation.

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