2022/06/15 by Maurice Jakesch, Jeffrey T. Hancock, Jeffrey Hancock +1 · 3 voices · 37 citations
Computer Science · Psychology · Social Sciences · #Authorship Attribution and Profiling #Deception detection and forensic psychology #Misinformation and Its Impacts #cs.AI #cs.CL #cs.CY #cs.HC
paper · pdf · doi:10.1073/pnas.2208839120
arxiv published 2022/06/15 · openalex publication_date 2023/03/07 · arxiv updated 2023/03/14 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/29
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.