How Does AI Improve Human Decision-Making? Evidence from the AI-Powered Go Program
Open accessAbstract
How does AI improve human decision-making? Answering this question is challenging because it is difficult to assess the quality of each decision and to disentangle AI’s influence on decisions. We study professional Go games, which provide a unique opportunity to overcome such challenges. In 2016 an AI-powered Go program (APG) unexpectedly beat the best human player, surpassing the best human knowledge and skills accumulated over thousands of years. To investigate the impact of APGs, we compare human moves to AI’s superior solutions, before and after the initial public release of an APG. Our analysis of 750,990 moves in 25,033 games by 1,242 professional players reveals that APGs significantly improved the quality of the players’ moves as measured by the changes in winning probability with each move. We also show that the key mechanisms are reductions in the number of human errors and in the magnitude of the most critical mistake during the game. Interestingly, the improvement is most prominent in the early stage of a game when uncertainty is higher. Further, young players—who are more open to and better able to utilize APG—benefit more than senior players, suggesting generational inequality in AI adoption and utilization.
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Human decision quality in Go showed very little improvement from 1950 to 2016, followed by a sudden improvement after the introduction of AlphaGo in March 2016.
“The decision quality, as measured by an open-source variant of AlphaGo Zero, showed very little improvement in human gameplay from 1950 to 2016, followed by a sudden improvement after the introduction of AlphaGo in March 2016 (refs.32,33) (Fig. 3b).”
From Machine culture (2023) · cites this paper as [32]
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- Authors
- Choi, S., Kim, N., Kim, J., & Kang, H.
- Affiliations
- University at Albany, State University of New York (US)
- Yonsei University (KR)
- Harvard University (US)
- University of Southern California (US)
- Corresponding author
- Kim, Namil
- Venue
- SSRN Electronic Journal
- Publisher
- Elsevier BV
- Published
- 2021
- ISSN
- 1556-5068 (ISSN-L)
- OpenAlex
- W3186551756
- Semantic Scholar
- 67675737d9092ff6ff63f8b1c1e1cac7dcae1034
- Cited by
- 30
- References
- 114
- Citations by year
- 2026: 3 · 2025: 11 · 2024: 9 · 2023: 6 · 2022: 1
- Type
- posted content
- Language
- en
- Open access
- green
- doi.org
- Locations
- SSRN Electronic Journal (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence in Games
- Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
- Social Sciences › Psychology › Developmental and Educational Psychology › Sport Psychology and Performance
- Keywords
- AI-assisted decision-making, human-AI interaction, Go, professional Go players, AI adoption in organizations, skill level differences, youth players, uncertainty
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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