论文标题
衡量人类对AI的适应决策：应用Alphago后评估更改的应用
Measuring Human Adaptation to AI in Decision Making: Application to Evaluate Changes after AlphaGo
论文作者
论文摘要
在越来越多的领域中，人类专家有望在具有出色的决策能力的情况下学习并适应AI。但是，我们如何量化这种人类对AI的适应？我们开发了一种简单的人类适应AI的方法，并在两个案例研究中测试了其有用性。在研究1中，我们分析了专业GO参与者做出的130万行动决定，发现对AI（学习）的积极形式发生在玩家可以观察AI的推理过程之后，而不是仅仅是AI的行动。这些基于我们衡量标准的发现突出了解释性对人类学习的重要性。在研究2中，我们测试了我们的措施是否足够敏感，可以捕获对AI的负面形式（由AI作弊），这发生在专业GO参与者之间的比赛中。我们讨论了我们在GO以外的领域中的应用程序，尤其是在AI决策能力可能会超过人类专家的领域。
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.
