How Does Artificial Intelligence Improve Human Decision-Making? Evidence from the AI-Powered Go Program
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866913642952261632 |
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| author | Choi, Sukwoong Kang, Hyo Kim, Namil Kim, Junsik |
| author_facet | Choi, Sukwoong Kang, Hyo Kim, Namil Kim, Junsik |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2310_08704 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | How Does Artificial Intelligence Improve Human Decision-Making? Evidence from the AI-Powered Go Program Choi, Sukwoong Kang, Hyo Kim, Namil Kim, Junsik General Economics Economics 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. |
| title | How Does Artificial Intelligence Improve Human Decision-Making? Evidence from the AI-Powered Go Program |
| topic | General Economics Economics |
| url | https://arxiv.org/abs/2310.08704 |