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.

翻译：人工智能（AI）系统正被越来越多地用于提供建议，以辅助人类在医疗保健、刑事司法和金融等广泛领域中的决策。基于当前实践中算法建议作为决策流程中的恒定元素提供给人类用户这一做法的局限性，本文提出了一个关键问题：算法应在何时提供建议？我们提出了一种新型AI系统设计，其中算法以双向方式与人类用户交互，并旨在仅在可能对用户决策产生有益影响时提供建议。大规模实验结果表明，与固定的、非交互式的建议方法相比，我们的建议方法能够在用户需要时提供建议，并显著改善人类决策。该方法在促进人类学习、保持人类决策者互补优势以及提高对建议的积极响应方面也具有额外优势。