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The AI Ghostwriter Effect: When Users Do Not Perceive Ownership of AI-Generated Text But Self-Declare as Authors

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arxiv 2303.03283 v2 pith:DYHEGS3H submitted 2023-03-06 cs.HC cs.CL

classification cs.HCcs.CL
keywords authorshipghostwritersownershipai-generatedeffectghostwriterhumanhuman-ai
verification ladder T0 review T1 audit T2 compute T3 formal
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Human-AI interaction in text production increases complexity in authorship. In two empirical studies (n1 = 30 & n2 = 96), we investigate authorship and ownership in human-AI collaboration for personalized language generation. We show an AI Ghostwriter Effect: Users do not consider themselves the owners and authors of AI-generated text but refrain from publicly declaring AI authorship. Personalization of AI-generated texts did not impact the AI Ghostwriter Effect, and higher levels of participants' influence on texts increased their sense of ownership. Participants were more likely to attribute ownership to supposedly human ghostwriters than AI ghostwriters, resulting in a higher ownership-authorship discrepancy for human ghostwriters. Rationalizations for authorship in AI ghostwriters and human ghostwriters were similar. We discuss how our findings relate to psychological ownership and human-AI interaction to lay the foundations for adapting authorship frameworks and user interfaces in AI in text-generation tasks.

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Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Timing Matters: How Using LLMs at Different Timings Influences Writers' Perceptions and Ideation Outcomes in AI-Assisted Ideation

    cs.HC 2025-02 conditional novelty 5.0 of 10

    Using an LLM after independent ideation preserves writers' autonomy, ownership, and creative self-efficacy compared to using it from the start, while reducing overlap with AI-generated ideas.

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