论文标题
学习何时为人类决策者提供建议
Learning When to Advise Human Decision Makers
论文作者
论文摘要
人工智能（AI）系统越来越多地用于提供建议，以促进在医疗保健，刑事司法和金融等广泛领域的人类决策。受到当前实践的局限性的限制，在本文中向人类用户提供了算法建议，这是决策管道中不断的要素，在本文中，我们提出了一个问题，即算法何时应该提供建议？我们提出了一种新型的AI系统设计，其中该算法以双向方式与人类用户进行交互，并旨在仅在可能对用户做出决定时有利于建议时提供建议。大规模实验的结果表明，与固定的非交互性，建议的方法相比，我们的建议方法可以在需要时提供建议，并显着改善人类决策。这种方法在促进人类学习，保留人类决策者的互补优势以及对建议的更积极反应方面具有其他优势。
Artificial intelligence (AI) systems are increasingly used for providing advice to facilitate human decision making in a wide range of domains, such as healthcare, criminal justice, and finance. Motivated by limitations of the current practice where algorithmic advice is provided to human users as a constant element in the decision-making pipeline, in this paper we raise the question of when should algorithms provide advice? We propose a novel design of AI systems in which the algorithm interacts with the human user in a two-sided manner and aims to provide advice only when it is likely to be beneficial for the user in making their decision. The results of a large-scale experiment show that our advising approach manages to provide advice at times of need and to significantly improve human decision making compared to fixed, non-interactive, advising approaches. This approach has additional advantages in facilitating human learning, preserving complementary strengths of human decision makers, and leading to more positive responsiveness to the advice.
