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.

通过 Democratic AI 方法，使用强化学习算法在参与者中探讨和设计社交机制来解决人工智能与人类价值观的对齐问题，有效缓解了财富分配不均，制止了搭便车行为并成功地赢得了大众的信任和认可。