黎樟浩,塔娜

• 1:广州大学新闻与传播学院
• 2:中国人民大学新闻学院
• 3:中国人民大学人工智能治理研究院

摘要(Abstract)：

生成式人工智能日益成为公众获取信息的重要工具，其基于用户特征进行内容定制的个性化说服能力尤为突出，同时也引发了对算法偏见的广泛关注。本文以国内三个主流生成式人工智能平台为研究对象，围绕公共健康、公共政策与社会伦理等争议性议题，构建涵盖性别、年龄等六个人口属性维度共96种用户画像，并据此设计提示词以评估生成内容的说服力差异。研究发现：在相同用户画像条件下，针对不同立场生成的文本存在显著的说服力差异；不同的用户画像显著影响生成文本的说服力；且人口属性对不同立场倾向的内容说服力具有调节作用。据此，本文提出“选择性说服”,揭示了生成式人工智能在争议性议题中的结构性立场偏见。这一机制体现为，生成式人工智能为不同群体或不同立场生成说服力具有显著差异的内容，可能潜在地参与公众态度形塑与社会共识构建的过程，并加剧数字不平等与群体极化等社会风险。

关键词(KeyWords)： 生成式人工智能;选择性说服;个性化说服;算法审计;数字不平等

Abstract:

Keywords:

基金项目(Foundation): 新一代人工智能国家科技重大专项“人工智能社会实验伦理、评估与标准化研究”(项目编号:2023ZD0121600)课题四“智能社会治理仿真演化模型研究”(项目编号:2023ZD0121604);; 中央高校基本科研业务费专项资金资助的阶段性成果

作者(Author): 黎樟浩,塔娜

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