Learning When to Advise Human Decision Makers
Learning When to Advise Human Decision Makers
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DOI：
10.48550/arxiv.2209.13578
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发表时间：
2022-09
期刊：
影响因子：
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通讯作者：
Gali Noti;Yiling Chen
中科院分区：
文献类型：
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作者：
Gali Noti;Yiling Chen
人工智能（AI）系统越来越多地用于提供建议，以促进人类在医疗保健、刑事司法和金融等广泛领域的决策。出于当前实践的局限性，算法的建议提供给人类用户作为一个恒定的元素在决策管道，在本文中，我们提出的问题时，应该算法提供建议？我们提出了一种新的AI系统设计，其中算法以双边方式与人类用户交互，并且仅在可能有利于用户做出决策时提供建议。一个大规模的实验结果表明，我们的建议方法管理，以提供建议，在需要的时候，并显着改善人类的决策相比，固定的，非交互式的，建议的方法。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做出更积极的响应方面具有额外的优势。
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.