  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.
