Measuring an adaptive change in human decision-making from AI: Application to evaluate changes after AlphaGo

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 Case 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 AI's reasoning processes rather than mere actions of AI. In Case Study 2, we test whether our measure is sensitive enough 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.