Human-centred mechanism design with Democratic AI

Raphael Koster, Jan Balaguer, Andrea Tacchetti, Ari Weinstein, Tina Zhu, Oliver Hauser, Duncan Williams, Lucy Campbell-Gillingham, Phoebe Thacker, Matthew Botvinick and Christopher Summerfield ()
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Raphael Koster: Deepmind
Jan Balaguer: Deepmind
Andrea Tacchetti: Deepmind
Ari Weinstein: Deepmind
Tina Zhu: Deepmind
Duncan Williams: Deepmind
Lucy Campbell-Gillingham: Deepmind
Phoebe Thacker: Deepmind
Matthew Botvinick: Deepmind
Christopher Summerfield: Deepmind

Nature Human Behaviour, 2022, vol. 6, issue 10, 1398-1407

Abstract: 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.

Date: 2022
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DOI: 10.1038/s41562-022-01383-x

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