Right agent, wrong level of hedonism: How high (vs low) hedonic values in AI-performed tasks lead to decreased perceptions of humanlikeness, warmth, and less consumer support

享乐主义 心理学 模式(遗传算法) 感知 社会心理学 调解 认知心理学 任务(项目管理) 计算机科学 机器学习 政治学 经济 神经科学 管理 法学
作者
MEHMET YANIT,Metehan Yanit,Fang Wan
出处
期刊:Computers in Human Behavior [Elsevier BV]
卷期号:147: 107870-107870 被引量:10
标识
DOI:10.1016/j.chb.2023.107870
摘要

Reasons of reactions towards artificial intelligence (AI) performing hedonic tasks invites attention. This research argues that the reference of comparison to assess AI's suitability for the task shifts from machine schema to human schema as the hedonic value of the task increases, leading to the decrease in perceived humanlikeness, perceived warmth, and support for the AI, subsequently. Four studies, including a big data study on GitHub and three experiments, have shown that people are less supportive of AI and its creators when AI performs highly (vs low) hedonic tasks. This effect is mediated by the perceived humanlikeness of AI, since it affects AI's perceived warmth in a way that decreasing perceived humanlikeness when AI is assigned to the tasks with high (vs low) hedonic values decreases the perceived warmth of the AI. Decreased perceived warmth, in turn, lowers people's support for the AI and its creators. This serial mediation mechanism remains robust when AI's baseline humanlikeness increases, when the hedonic value of AI's method of performing the task, rather than the task itself is manipulated, and when the perceived competence and fairness are tested as alternative paths in parallel.
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