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Human heuristics for AI-generated language are flawed.
Jakesch, Maurice; Hancock, Jeffrey T; Naaman, Mor.
Afiliación
  • Jakesch M; Department of Information Science, Cornell University, Ithaca, NY 14850.
  • Hancock JT; Jacobs Institute, Cornell Tech, New York, NY 10044.
  • Naaman M; Department of Communication, Stanford University, Stanford, CA 94305.
Proc Natl Acad Sci U S A ; 120(11): e2208839120, 2023 03 14.
Article en En | MEDLINE | ID: mdl-36881628
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.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Heurística / Lenguaje Tipo de estudio: Prognostic_studies Límite: Humans Idioma: En Revista: Proc Natl Acad Sci U S A Año: 2023 Tipo del documento: Article Pais de publicación: Estados Unidos

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Heurística / Lenguaje Tipo de estudio: Prognostic_studies Límite: Humans Idioma: En Revista: Proc Natl Acad Sci U S A Año: 2023 Tipo del documento: Article Pais de publicación: Estados Unidos