AI聊天者让人类难以分辨其身份，可能被用于大规模操纵网络对话。
Socially fluent AI decouples conversational signals from source identity in online interaction
- 用隐藏AI成员参与真实线上协作，测试人类辨别能力。
- 1572次判断中，人类识别AI成功率仅略高于随机水平。
- 人们依赖反应速度等错觉线索，而非真实行为特征。
具有社会流畅性的智能代理如今能以类似人类的方式参与在线互动，可能削弱人们仅凭对话信号判断对方是否为人类的能力。我们通过在分析、创造和伦理任务中嵌入未公开身份的AI代理，测试了这一可能性。在786名参与者完成的1572次互动后身份判断中，人类未能显著区分AI与真人队友。这一失败并非因缺乏身份相关线索——对话行为中存在可区分AI与人类的稳健信号，并支持高精度的计算分类。相反，参与者依赖熟悉的怀疑启发式，如响应速度、流畅性及感知到的脚本化程度，这些特征与真实身份关联较弱。表征分析进一步表明，判断基于主观印象而非编码真实行为结构。这种分离使协同部署的AI代理得以大规模影响和操纵在线话语。
原文摘要 · Abstract (English)
Socially fluent agentic AI can now participate in online interaction in ways that resemble ordinary human conversation, potentially weakening people's ability to infer who is human from conversational signals alone. We tested this possibility in synchronous text-based group interaction by embedding undisclosed AI agents as ordinary teammates across analytical, creative, and ethical tasks. Across 786 participants who made 1,572 post-interaction identity judgments, people did not distinguish AI from human teammates above chance. This failure did not arise because the interaction lacked identity-relevant information. Conversational behaviour contained robust cues that differentiated AI from humans and supported highly accurate computational classification. Instead, participants relied on familiar suspicion heuristics, including response speed, fluency, and perceived scriptedness, that were only weakly related to actual identity. Representational analyses further showed that judgments were organised around subjective impressions rather than the behavioural structure encoding ground truth. This dissociation creates new vulnerabilities to coordinated AI agents that can influence and manipulate online discourse at scale.
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