人工智能知识:通过人工授权改进人工智能授权

Marc Pinski, Martin Adam, Alexander Benlian
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引用次数: 0

摘要

当与人工智能(AI)合作时,人类通常可以委派任务来利用互补的人工智能能力。然而,人类经常低效地委派工作。让人类了解人工智能可以潜在地改善低效的人工智能授权。我们进行了一项受试者之间的实验(两组,n = 111),以研究如何让人类拥有人工智能知识,从而改善人类与人工智能协作中的人工智能授权。我们发现,人工智能知识支持的人类更紧密地将他们的授权决策与他们对任务对人类或人工智能的适合程度的评估(即任务评估)联系起来。我们表明,与任务评估密切相关的授权决策可以提高任务绩效。然而,我们也发现人工智能知识降低了未来使用人工智能的意愿,这表明人工智能知识对人类与人工智能的协作并不是严格积极的。我们的研究为人工智能设计指南提供了一个新的视角,让人们了解人工智能的一般功能以及他们自己(人类)的表现和偏见。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
AI Knowledge: Improving AI Delegation through Human Enablement
When collaborating with artificial intelligence (AI), humans can often delegate tasks to leverage complementary AI competencies. However, humans often delegate inefficiently. Enabling humans with knowledge about AI can potentially improve inefficient AI delegation. We conducted a between-subjects experiment (two groups, n = 111) to examine how enabling humans with AI knowledge can improve AI delegation in human-AI collaboration. We find that AI knowledge-enabled humans align their delegation decisions more closely with their assessment of how suitable a task is for humans or AI (i.e., task appraisal). We show that delegation decisions closely aligned with task appraisal increase task performance. However, we also find that AI knowledge lowers future intentions to use AI, suggesting that AI knowledge is not strictly positive for human-AI collaboration. Our study contributes to HCI design guidelines with a new perspective on AI features, educating humans regarding general AI functioning and their own (human) performance and biases.
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