任务(项目管理)
过程(计算)
感知
人工智能
知识管理
人工智能应用
工作(物理)
计算机科学
医学
心理学
管理
机械工程
操作系统
工程类
经济
神经科学
作者
Yosef Berlyand,Ali S. Raja,Stephen C. Dorner,Anand M. Prabhakar,Jonathan D Sonis,Ravi V. Gottumukkala,Marc D. Succi,Brian J. Yun
标识
DOI:10.1016/j.ajem.2018.01.017
摘要
Integrating artificial intelligence (AI) systems into decision-making tasks attempts to assist people by augmenting or complementing their abilities and ultimately improve task performance. However, when considering recommendations from modern “black box” intelligent systems, users are confronted with the decision of accepting or overriding AI’s recommendations. These decisions are even more challenging to make when there exists a significant knowledge imbalance between the users and the AI system—namely, when people lack necessary task knowledge and are therefore unable to accurately complete the task on their own. In this work, we aim to understand people’s behavior in AI-assisted decision-making tasks when faced with the challenge of knowledge imbalance and explore whether involving users in an AI’s prediction generation process makes them more willing to follow the AI’s recommendations and enhances their perception of collaboration. Our empirical study reveals that the involvement of users in generating AI recommendations during a task with notable knowledge imbalance causes them to be more willing to agree with the AI’s suggestions and to perceive the AI agent and their collaboration as a team more positively.
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