建议(编程)
任务(项目管理)
通知
计算机科学
算法
度量(数据仓库)
光学(聚焦)
管理科学
心理学
应用心理学
数据挖掘
工程类
政治学
物理
程序设计语言
法学
系统工程
光学
作者
Esther Kaufmann,Alvaro Chacon,Edgar E. Kausel,Nicolás Herrera,Tomás Reyes
标识
DOI:10.1016/j.dim.2023.100040
摘要
Due to digitalization resulting in artificial intelligence advice, there are increasing studies on advice taking, exploring individual and task-relevant factors associated with the acceptance of algorithm advice. However, to our notice, there are no reviews of studies on the acceptance of algorithm advice that focus explicitly on a task level that consider methodological features and provide a quantitative measure of algorithm acceptance. Our review closes these research gaps. We evaluated 44 studies, 122 tasks, and 89,751 participants. Our review shows that algorithm aversion is present in 75% of the 122 considered tasks. In addition, our quantified measures underscore some shortcomings by the underrepresented individual, task, or methodological characteristics—for example, the expertise of advice takers and longitudinal studies. Finally, we provide valuable recommendations to continue research on algorithm acceptance.
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