Human-centered mechanism design with Democratic AI
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Abstract (arXiv)
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.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 11 of its 37 references that are in the map. First posted on arXiv 27 Jan 2022.
Builds on
11 of 37 references.
References · 11
- 2017Deep reinforcement learning from human preferencescited by 3,846
- 2017Towards A Rigorous Science of Interpretable Machine Learningcited by 1,400
- 2020Artificial Intelligence, Values and Alignmentcited by 443
- 2019On the Utility of Learning about Humans for Human-AI Coordinationcited by 345
- 2020Open Problems in Cooperative AIcited by 167
- 2020Decolonial AI: Decolonial Theory as Sociotechnical Foresight in Artificial Intelligencecited by 100
- 2020The AI Economist: Improving Equality and Productivity with AI-Driven Tax Policiescited by 75
- 2017A Voting-Based System for Ethical Decision Makingcited by 55
- 2020Imitating Interactive Intelligencecited by 55
- 2017Society-in-the-Loop: Programming the Algorithmic Social Contractcited by 35
- 2018Mechanism Design for Social Goodcited by 13
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Citing papers per year: 2022: 1; 2023: 0; 2024: 0; 2025: 1; 2026: 1.
3 papers.