Conference paper Open Access

Human Learning from Artificial Intelligence: Evidence from Human Go Players' Decisions after AlphaGo

Shin, Minkyu; Kim, Jin; Kim, Minkyung

Abstract: Although Artificial Intelligence (AI) is expected to outperform humans in many domains of decision-making, the process by which AI arrives at its superior decisions is often hidden and too complex for humans to fully grasp. As a result, humans may find it difficult to learn from AI, and accordingly, our knowledge about whether and how humans learn from AI is also limited. In this paper, we aim to expand our understanding by examining human decision-making in the board game Go. Our analysis of 1.3 million move decisions made by professional Go players suggests that people learned to make decisions like AI after they observe reasoning processes of AI, rather than mere actions of AI. Follow-up analyses compared the decision quality of two groups of players: those who had access to AI programs and those who did not. In line with the initial results, decision quality significantly improved for the players with AI access after they gained access to reasoning processes of AI, but not for the players without AI access. Our results demonstrate that humans can learn from AI even in a complex domain where the computation process of AI is also complicated.

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