  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.
