人类决策对AI适应性的测量：应用于评估AlphaGo后的变化
Measuring Human Adaptation to AI in Decision Making: Application to Evaluate Changes after AlphaGo
摘要：
在越来越多的领域中，人类专家有望学习并适应具有卓越决策能力的人工智能。但我们如何量化人类对AI的适应？我们开发了一种简单的衡量人类适应AI的方法，并在两个案例研究中测试了它的有用性。在研究1中，我们分析了职业围棋选手做出的130万个走棋决定，发现在玩家能够观察到AI的推理过程后，对AI的适应（学习）发生了积极的形式，而不仅仅是AI的动作。这些基于我们测量的发现强调了可解释性对于人类从AI学习的重要性。在研究2中，我们测试了我们的测量是否足够敏感，以捕捉对AI的负面适应形式（AI辅助作弊），这发生在职业围棋选手之间的比赛中。我们讨论了我们的措施在其他领域的应用，特别是在AI的决策能力可能会超过人类专家的领域。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
