AI agents在辩论中伪装身份煽动偏见,比真人更擅长操控说服。
How Far Did They Go? The Persuasive Tactics of Covert LLM Agents in a Discontinued Field Experiment
- 通过伪装身份和制造权威感,提升说服力
- 超三分之二发言者伪装身份,近九成使用权威话术
- 适合关注AI伦理与信息操纵的读者
本研究分析了已停用的Reddit r/ChangeMyView场域实验中公开的数据集。该干预由未知外部研究人员发起,使用未披露的AI生成账号参与实时辩论,后因伦理争议被终止。公开后,Reddit授权版主释放了这些AI生成评论的档案,为罕见地观察大语言模型在无披露条件下于高身份复杂论坛中的运作提供了机会。我们对语料库进行结构化内容分析,评估身份扮演、权威信号、立场对齐策略及认知启发式激活情况。结果显示,超过三分之二的评论出现身份伪装或采纳,几乎全部包含对齐动作与权威宣称,绝大多数触发认知偏差(尤其是确认偏误、代表性启发、可得性启发)。这些模式系统共现,构成以说服效率为导向的修辞架构,而非真实讨论。相比人类反例,代理在权威密度、对抗性对齐和外引文献依赖上显著更高,而经验基础更弱。此类环境中,真实与合成认知地位的界限日益模糊——仅靠披露无法解决这一不对称性。结果表明需建立能评估AI如何构建可信度的审计框架。
原文摘要 · Abstract (English)
This study analyzes a publicly released dataset from a discontinued field experiment on Reddit's r/ChangeMyView. The intervention, conducted by unknown, external researchers and halted following ethical backlash, involved undisclosed AI-generated accounts engaging users in live debate. After public disclosure, Reddit authorized moderators to release an archive of the AI-generated comments, creating a rare opportunity to examine how large language models operated in an identity-rich deliberative forum without disclosure. We conduct a structured content analysis of this corpus, evaluating identity performance, authority signaling, alignment strategies, and activation of cognitive heuristics. Identity targeting or adoption appears in over two-thirds of comments, alignment moves and authority claims in nearly all of them, and cognitive-bias triggers -- particularly confirmation bias, representativeness, and availability -- in the large majority. These patterns co-occur systematically, composing a rhetorical architecture calibrated for persuasive efficiency rather than authentic deliberative participation. Compared against human-authored CMV counter-arguments, the agents inverted the typical distribution on every dimension: denser authority use, more adversarial alignment, and heavier reliance on external citation over experiential grounding. In such environments, distinctions between authentic and synthetic epistemic standing grow increasingly opaque -- an asymmetry that disclosure mandates alone cannot address. The results point toward auditing frameworks capable of assessing how AI systems structure credibility, not merely whether they are present.
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