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How Does Artificial Intelligence Improve Human Decision-Making? Evidence from the AI-Powered Go Program

Author

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  • Sukwoong Choi
  • Hyo Kang
  • Namil Kim
  • Junsik Kim

Abstract

We study how humans learn from AI, leveraging an introduction of an AI-powered Go program (APG) that unexpectedly outperformed the best professional player. We compare the move quality of professional players to APG's superior solutions around its public release. Our analysis of 749,190 moves demonstrates significant improvements in players' move quality, especially in the early stages of the game where uncertainty is highest. This improvement was accompanied by a higher alignment with AI's suggestions and a decreased number and magnitude of errors. Young players show greater improvement, suggesting potential inequality in learning from AI. Further, while players of all skill levels benefit, less skilled players gain higher marginal benefits. These findings have implications for managers seeking to adopt and utilize AI in their organizations.

Suggested Citation

  • Sukwoong Choi & Hyo Kang & Namil Kim & Junsik Kim, 2023. "How Does Artificial Intelligence Improve Human Decision-Making? Evidence from the AI-Powered Go Program," Papers 2310.08704, arXiv.org, revised Jan 2025.
    • Handle: RePEc:arx:papers:2310.08704
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    Cited by:

    1. Hyungseok David Yoon & Mustapha Belkhouja & Luis Alfonso Dau, 2025. "Privacy protection laws, national culture, and artificial intelligence innovation around the world," Journal of International Business Studies, Palgrave Macmillan;Academy of International Business, vol. 56(7), pages 853-873, September.
    2. Wang, Zheng & Liu, Hongchao, 2026. "Artificial intelligence and the historical performance expectation gap: Evidence from the moderating role of monetary easing," Economic Analysis and Policy, Elsevier, vol. 89(C), pages 1077-1092.
    3. Han Li & Feng Tian, 2026. "Advancing Decision-Making through AI-Human Collaboration: A Systematic Review and Conceptual Framework," Group Decision and Negotiation, Springer, vol. 35(2), pages 1-24, June.
    4. Zhuoyan Lao, & Guan, Yimeng, 2026. "Cybersecurity legislation and corporate AI adoption: Evidence from China—A pre-registered report," Pacific-Basin Finance Journal, Elsevier, vol. 95(C).
    5. Li, Junze, 2026. "Artificial intelligence innovation and financial report quality," International Review of Economics & Finance, Elsevier, vol. 105(C).
    6. Gelsing, Daniel, 2026. "Construed Realities : CEO Characteristics and Cognitive Biases in Corporate Scope Activities," Other publications TiSEM e0994a90-3466-4fd8-802d-a, Tilburg University, School of Economics and Management.

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