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Measuring Human Adaptation to AI in Decision Making: Application to Evaluate Changes after AlphaGo

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  • Minkyu Shin
  • Jin Kim
  • Minkyung Kim

Abstract

Across a growing number of domains, human experts are expected to learn from and adapt to AI with superior decision making abilities. But how can we quantify such human adaptation to AI? We develop a simple measure of human adaptation to AI and test its usefulness in two case studies. In Study 1, we analyze 1.3 million move decisions made by professional Go players and find that a positive form of adaptation to AI (learning) occurred after the players could observe the reasoning processes of AI, rather than mere actions of AI. These findings based on our measure highlight the importance of explainability for human learning from AI. In Study 2, we test whether our measure is sufficiently sensitive to capture a negative form of adaptation to AI (cheating aided by AI), which occurred in a match between professional Go players. We discuss our measure's applications in domains other than Go, especially in domains in which AI's decision making ability will likely surpass that of human experts.

Suggested Citation

  • Minkyu Shin & Jin Kim & Minkyung Kim, 2020. "Measuring Human Adaptation to AI in Decision Making: Application to Evaluate Changes after AlphaGo," Papers 2012.15035, arXiv.org, revised Jan 2021.
    • Handle: RePEc:arx:papers:2012.15035
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    1. Anthony Strittmatter & Uwe Sunde & Dainis Zegners, 2020. "Life cycle patterns of cognitive performance over the long run," Proceedings of the National Academy of Sciences, Proceedings of the National Academy of Sciences, vol. 117(44), pages 27255-27261, November.
    2. Pascale Waelti & Anthony Dickinson & Wolfram Schultz, 2001. "Dopamine responses comply with basic assumptions of formal learning theory," Nature, Nature, vol. 412(6842), pages 43-48, July.
    3. David Silver & Julian Schrittwieser & Karen Simonyan & Ioannis Antonoglou & Aja Huang & Arthur Guez & Thomas Hubert & Lucas Baker & Matthew Lai & Adrian Bolton & Yutian Chen & Timothy Lillicrap & Fan , 2017. "Mastering the game of Go without human knowledge," Nature, Nature, vol. 550(7676), pages 354-359, October.
    4. Mitsuru Igami, 0. "Artificial intelligence as structural estimation: Deep Blue, Bonanza, and AlphaGo," Econometrics Journal, Royal Economic Society, vol. 23(3), pages 1-24.
    5. Will Dabney & Zeb Kurth-Nelson & Naoshige Uchida & Clara Kwon Starkweather & Demis Hassabis & Rémi Munos & Matthew Botvinick, 2020. "A distributional code for value in dopamine-based reinforcement learning," Nature, Nature, vol. 577(7792), pages 671-675, January.
    6. John Rust, 2019. "Has Dynamic Programming Improved Decision Making?," Annual Review of Economics, Annual Reviews, vol. 11(1), pages 833-858, August.
    7. Mitsuru Igami, 2020. "Artificial intelligence as structural estimation: Deep Blue, Bonanza, and AlphaGo," The Econometrics Journal, Royal Economic Society, vol. 23(3), pages 1-24.
    8. David Silver & Aja Huang & Chris J. Maddison & Arthur Guez & Laurent Sifre & George van den Driessche & Julian Schrittwieser & Ioannis Antonoglou & Veda Panneershelvam & Marc Lanctot & Sander Dieleman, 2016. "Mastering the game of Go with deep neural networks and tree search," Nature, Nature, vol. 529(7587), pages 484-489, January.
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