Will You Accept the AI Recommendation? Predicting Human Behavior in AI-Assisted Decision Making

Will You Accept the AI Recommendation? Predicting Human Behavior in AI-Assisted Decision Making
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你会接受人工智能的推荐吗?

DOI:
10.1145/3485447.3512240
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发表时间:
2022
期刊:
Proceedings of the 2022 ACM Web Conference (WWW
影响因子:
--
通讯作者:
Yin, Ming
Yin, Ming
中科院分区:
--
文献类型:
--
作者:
Wang, Xinru;Lu, Zhuoran;Yin, Ming

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互联网用户每天在网上做出许多决定。随着人工智能的快速发展,人工智能辅助决策-人工智能模型提供决策建议和信心,而人类做出最终决策-已成为人类与人工智能协作的新范式。在本文中,我们的目标是定量地了解人类决策者是否以及何时会采用AI模型的建议。我们通过将人类决策者在每个决策任务中的认知过程分解为两个部分来定义人类行为模型的空间:效用部分(即,评估不同动作的效用)和选择组件(即,选择要采取的动作),并且我们在模型空间中执行系统搜索以识别最适合真实世界人类行为数据的模型。我们的研究结果表明,在人工智能辅助决策中,人类决策者的效用评估和行动选择受到他们自己对决策任务的判断和信心的影响。此外,人类决策者表现出扭曲效用评估的决策信心的倾向。最后,我们还分析了人类对人工智能建议的采纳行为的差异,因为决策的利害关系不同。
Internet users make numerous decisions online on a daily basis. With the rapid advances in AI recently, AI-assisted decision making—in which an AI model provides decision recommendations and confidence, while the humans make the final decisions—has emerged as a new paradigm of human-AI collaboration. In this paper, we aim at obtaining a quantitative understanding of whether and when would human decision makers adopt the AI model’s recommendations. We define a space of human behavior models by decomposing the human decision maker’s cognitive process in each decision-making task into two components: the utility component (i.e., evaluate the utility of different actions) and the selection component (i.e., select an action to take), and we perform a systematic search in the model space to identify the model that fits real-world human behavior data the best. Our results highlight that in AI-assisted decision making, human decision makers’ utility evaluation and action selection are influenced by their own judgement and confidence on the decision-making task. Further, human decision makers exhibit a tendency to distort the decision confidence in utility evaluations. Finally, we also analyze the differences in humans’ adoption behavior of AI recommendations as the stakes of the decisions vary.
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