任务特定算法建议接受：综述和未来研究方向

Data and information management Pub Date : 2023-09-01 DOI:10.1016/j.dim.2023.100040

Esther Kaufmann , Alvaro Chacon , Edgar E. Kausel , Nicolas Herrera , Tomas Reyes

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引用次数: 1

摘要

由于数字化带来了人工智能建议，关于建议接受的研究越来越多，探索与接受算法建议相关的个人和任务相关因素。然而，我们注意到，没有关于算法建议接受度的研究综述，这些研究明确地关注任务级别，考虑方法特征并提供算法接受度的定量度量。我们的综述弥补了这些研究空白。我们评估了44项研究、122项任务和89,751名参与者。我们的回顾显示，在122个考虑的任务中，有75%存在算法厌恶。此外，我们的量化测量强调了由于个人、任务或方法特征的代表性不足而造成的一些缺陷，例如，建议接受者的专业知识和纵向研究。最后，对算法可接受性的进一步研究提出了有价值的建议。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Task-specific algorithm advice acceptance: A review and directions for future research

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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