Human communication is increasingly intermixed with language generated by AI. Across chat, email, and social media, AI systems suggest words, complete sentences, or produce entire conversations. AI-generated language is often not identified as such but presented as language written by humans, raising concerns about novel forms of deception and manipulation. Here, we study how humans discern whether verbal self-presentations, one of the most personal and consequential forms of language, were generated by AI. In six experiments, participants (N = 4,600) were unable to detect self-presentations generated by state-of-the-art AI language models in professional, hospitality, and dating contexts. A computational analysis of language features shows that human judgments of AI-generated language are hindered by intuitive but flawed heuristics such as associating first-person pronouns, use of contractions, or family topics with human-written language. We experimentally demonstrate that these heuristics make human judgment of AI-generated language predictable and manipulable, allowing AI systems to produce text perceived as "more human than human." We discuss solutions, such as AI accents, to reduce the deceptive potential of language generated by AI, limiting the subversion of human intuition.

翻译：人类沟通正日益与AI生成的语言交织在一起。在聊天、电子邮件和社交媒体中，AI系统不断建议词语、补全句子或生成完整对话。AI生成的语言通常不被识别为AI产物，而是以人类书写语言的形式呈现，引发了对新型欺骗与操纵的担忧。本研究探讨了人类如何辨别口头自我呈现——一种最个人化且具有重要后果的语言形式——是否由AI生成。通过六项实验（参与者共4600名），我们发现参与者无法在专业、接待和约会场景中检测出由最先进AI语言模型生成的自我呈现。对语言特征的计算分析表明，人类对AI生成语言的判断受到直觉但存在缺陷的启发式策略的阻碍，例如将第一人称代词、缩写形式或家庭话题与人类语言相关联。我们通过实验证明，这些启发式策略使得人类对AI生成语言的判断具有可预测性和可操纵性，从而使AI系统能够生成被视为"比人类更人性化"的文本。我们探讨了解决方案，例如添加"AI口音"，以减少AI生成语言的欺骗潜力，从而限制对人类直觉的颠覆。