对话式AI能否弱化“我们与他们”的界限？共同、双重及分离身份框架对支持移民的群体间帮助的影响
Can Conversational AI loosen Us-Versus-Them Boundaries? The Effects of Common, Dual, and Separate Identity Framings on Pro-Immigrant Intergroup Helping
浏览论文内容
中文总结 AI 辅助
该研究通过与GPT-4o开展五轮对话的预先注册实验，发现强调共同或双重身份的AI对话可弱化群体间界限，提升行动意愿，且效应在各调节变量中基本一致。
中文摘要 AI 辅助
移民潮加剧了许多国家的群体间紧张关系，传统的偏见减少项目难以规模化且日益受到美国政策的限制。这项预先注册的实验测试了对话式AI是否能改变多数群体成员对拉丁裔移民的分类方式和关联方式。基于共同内群体身份模型，658名非拉丁裔白人美国成年人组成的配额代表性全国样本与大语言模型（LLM，即GPT-4o）完成了五轮对话。该模型被指示以共同内群体身份（共享的美国身份）、双重身份（既是拉丁裔又是美国人）、分离身份（独特的文化边界）框架来呈现拉丁裔移民，或在控制条件下讨论不相关话题。这些操纵改变了分类：与控制条件相比，共同内群体身份和双重身份对话降低了分离分类，双重身份对话提高了双重分类。尽管对行为和支持多样性信念的直接影响不显著，但强调上级身份（共同内群体和双重身份）的条件下，行动意愿显著更高。路径模型进一步揭示了间接关联：这两种条件均降低了分离分类，而分离分类与更高的行动意愿相关。对对话记录的语义相似性分析证实，对话遵循了分配的叙事；参与者与共享身份语言的趋同与行动意愿正相关，与分离身份语言的趋同则与行动意愿负相关。这些效应在调节变量（闭合需求、经验开放性和政治倾向）中基本一致。研究结果表明，简短的AI对话可以弱化“我们与他们”的界限，同时凸显了认知重新分类与行为之间的差距。
英文摘要
Rising immigration has intensified intergroup tensions in many countries. Traditional bias-reduction programs remain difficult to scale and increasingly constrained by U.S. policy. This preregistered experiment tested whether conversational AI can shift how majority-group members categorize and relate to Latine immigrants. Drawing on the common ingroup identity model, a quota-representative national sample of 658 non-Latine White U.S. adults completed five rounds of dialogue with a LLM (GPT-4o). The model was instructed to frame Latine immigrants in terms of a common ingroup identity (a shared American identity), a dual identity (both Latine and American), or a separate identity (distinct cultural boundaries), or to discuss an unrelated topic in a control condition. The manipulations altered categorization: relative to control, common ingroup identity and dual identity conversations lowered separate categorization, and dual identity conversations raised dual categorization. Although direct effects on behavior and pro-diversity beliefs were nonsignificant, willingness to act was significantly higher in the conditions emphasizing a superordinate identity (common ingroup and dual identity). A path model further revealed indirect associations: both conditions reduced separate categorization, which in turn correlated with greater willingness to act. Semantic similarity analyses of the transcripts confirmed that conversations tracked their assigned narratives; participants' convergence with shared-identity language related positively, and with separate-identity language negatively, to willingness to act. These effects were largely consistent across moderators (need for closure, openness to experience, and political orientation). The findings show that brief AI conversations can loosen us-versus-them boundaries while underscoring the gap between cognitive recategorization and behavior.
