This study examines antecedents that drive platform users to manipulate artificial intelligence (AI) recommendation algorithms. Based on the persuasion knowledge model (PKM), survey data collected from YouTube and Instagram users reveal that AI manipulation intentions are positively affected by persuasion knowledge about AI and perceived interactivity. Perceived interactivity is associated with higher perceived benefits and lower perceived costs of AI manipulation, consequently affecting manipulation intentions. A multivariate analysis of variance shows variations in intentions to use different types of AI manipulation behaviors among users with varying levels of persuasion knowledge. The research contributes to the PKM and AI-human interaction literature.

中文翻译：

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了解用户的 AI 操纵意图：对 AI 推荐算法背景下前因的实证调查


本研究研究了驱动平台用户操纵人工智能 （AI） 推荐算法的前因。基于说服知识模型 （PKM），从 YouTube 和 Instagram 用户那里收集的调查数据显示，关于 AI 的说服知识和感知交互性对 AI 操纵意图有积极影响。感知交互性与 AI 操作的较高感知收益和较低感知成本相关，从而影响操作意图。多变量方差分析显示，具有不同说服知识水平的用户使用不同类型 AI 操作行为的意图存在差异。该研究为 PKM 和 AI -人类交互文献做出了贡献。