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Human-centred mechanism design with Democratic AI

Author

Listed:
  • Raphael Koster

    (Deepmind)

  • Jan Balaguer

    (Deepmind)

  • Andrea Tacchetti

    (Deepmind)

  • Ari Weinstein

    (Deepmind)

  • Tina Zhu

    (Deepmind)

  • Oliver Hauser

    (University of Exeter)

  • Duncan Williams

    (Deepmind)

  • Lucy Campbell-Gillingham

    (Deepmind)

  • Phoebe Thacker

    (Deepmind)

  • Matthew Botvinick

    (Deepmind
    University College London)

  • Christopher Summerfield

    (Deepmind
    University of Oxford)

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.

Suggested Citation

  • Raphael Koster & Jan Balaguer & Andrea Tacchetti & Ari Weinstein & Tina Zhu & Oliver Hauser & Duncan Williams & Lucy Campbell-Gillingham & Phoebe Thacker & Matthew Botvinick & Christopher Summerfield, 2022. "Human-centred mechanism design with Democratic AI," Nature Human Behaviour, Nature, vol. 6(10), pages 1398-1407, October.
    • Handle: RePEc:nat:nathum:v:6:y:2022:i:10:d:10.1038_s41562-022-01383-x
    DOI: 10.1038/s41562-022-01383-x
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    • Raphael Koster & Jan Balaguer & Andrea Tacchetti & Ari Weinstein & Tina Zhu & Oliver Hauser & Duncan Williams & Lucy Campbell-Gillingham & Phoebe Thacker & Matthew Botvinick & Christopher Summerfield, 2022. "Human-centered mechanism design with Democratic AI," Papers 2201.11441, arXiv.org.

    References listed on IDEAS

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    Cited by:

    1. Sætra, Henrik Skaug, 2023. "Generative AI: Here to stay, but for good?," Technology in Society, Elsevier, vol. 75(C).
    2. Leib, Margarita & Köbis, Nils & Rilke, Rainer Michael & Hagens, Marloes & Irlenbusch, Bernd, 2023. "Corrupted by Algorithms? How AI-Generated and Human-Written Advice Shape (Dis)Honesty," IZA Discussion Papers 16293, Institute of Labor Economics (IZA).
    3. Edwin Zhang & Sadie Zhao & Tonghan Wang & Safwan Hossain & Henry Gasztowtt & Stephan Zheng & David C. Parkes & Milind Tambe & Yiling Chen, 2024. "Social Environment Design," Papers 2402.14090, arXiv.org, revised Jun 2024.
    4. Jan Balaguer & Raphael Koster & Christopher Summerfield & Andrea Tacchetti, 2022. "The Good Shepherd: An Oracle Agent for Mechanism Design," Papers 2202.10135, arXiv.org.
    5. Kshama Dwarakanath & Svitlana Vyetrenko & Tucker Balch, 2024. "Empirical Equilibria in Agent-based Economic systems with Learning agents," Papers 2408.12038, arXiv.org.
    6. Yang, Nanyin & Palma, Marco & Drichoutis, Andreas C., 2023. "Humanization of Virtual Assistants and Delegation Choices," MPRA Paper 119275, University Library of Munich, Germany.
    7. Coeckelbergh, Mark & Sætra, Henrik Skaug, 2023. "Climate change and the political pathways of AI: The technocracy-democracy dilemma in light of artificial intelligence and human agency," Technology in Society, Elsevier, vol. 75(C).

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