Baines Julia I, Dalal Reeshad S, Ponce Lida P, Tsai Ho-Chun
Department of Psychology, George Mason University, Fairfax, VA, United States.
Department of Psychology, Illinois Institute of Technology, Chicago, IL, United States.
Front Psychol. 2024 Oct 8;15:1390182. doi: 10.3389/fpsyg.2024.1390182. eCollection 2024.
Despite considerable behavioral and organizational research on advice from human advisors, and despite the increasing study of artificial intelligence (AI) in organizational research, workplace-related applications, and popular discourse, an interdisciplinary review of advice from AI (vs. human) advisors has yet to be undertaken. We argue that the increasing adoption of AI to augment human decision-making would benefit from a framework that can characterize such interactions. Thus, the current research invokes judgment and decision-making research on advice from human advisors and uses a conceptual "fit"-based model to: (1) summarize how the characteristics of the AI advisor, human decision-maker, and advice environment influence advice exchanges and outcomes (including informed speculation about the durability of such findings in light of rapid advances in AI technology), (2) delineate future research directions (along with specific predictions), and (3) provide practical implications involving the use of AI advice by human decision-makers in applied settings.
尽管针对人类顾问提供的建议开展了大量行为学和组织学研究,尽管在组织研究、职场相关应用及大众讨论中对人工智能(AI)的研究日益增多,但尚未有人对人工智能顾问与人类顾问提供的建议进行跨学科综述。我们认为,越来越多地采用人工智能来辅助人类决策,若有一个能够描述此类交互的框架将大有裨益。因此,当前的研究借鉴了关于人类顾问提供建议的判断与决策研究,并使用一个基于概念“匹配”的模型来:(1)总结人工智能顾问、人类决策者及建议环境的特征如何影响建议交流及结果(包括鉴于人工智能技术的快速发展,对这些发现的持久性进行有根据的推测),(2)勾勒未来的研究方向(以及具体预测),(3)提供涉及人类决策者在应用场景中使用人工智能建议时的实际意义。