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Art-ificial Intelligence: The Effect of AI Disclosure on Evaluations of Creative Content

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  • Manav Raj
  • Justin Berg
  • Rob Seamans

Abstract

The emergence of generative AI technologies, such as OpenAI's ChatGPT chatbot, has expanded the scope of tasks that AI tools can accomplish and enabled AI-generated creative content. In this study, we explore how disclosure regarding the use of AI in the creation of creative content affects human evaluation of such content. In a series of pre-registered experimental studies, we show that AI disclosure has no meaningful effect on evaluation either for creative or descriptive short stories, but that AI disclosure has a negative effect on evaluations for emotionally evocative poems written in the first person. We interpret this result to suggest that reactions to AI-generated content may be negative when the content is viewed as distinctly "human." We discuss the implications of this work and outline planned pathways of research to better understand whether and when AI disclosure may affect the evaluation of creative content.

Suggested Citation

  • Manav Raj & Justin Berg & Rob Seamans, 2023. "Art-ificial Intelligence: The Effect of AI Disclosure on Evaluations of Creative Content," Papers 2303.06217, arXiv.org, revised Jun 2024.
    • Handle: RePEc:arx:papers:2303.06217
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    References listed on IDEAS

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    1. Seo Young Kim & Bernd H. Schmitt & Nadia M. Thalmann, 2019. "Eliza in the uncanny valley: anthropomorphizing consumer robots increases their perceived warmth but decreases liking," Marketing Letters, Springer, vol. 30(1), pages 1-12, March.
    2. Logg, Jennifer M. & Minson, Julia A. & Moore, Don A., 2019. "Algorithm appreciation: People prefer algorithmic to human judgment," Organizational Behavior and Human Decision Processes, Elsevier, vol. 151(C), pages 90-103.
    3. Xueming Luo & Siliang Tong & Zheng Fang & Zhe Qu, 2019. "Frontiers: Machines vs. Humans: The Impact of Artificial Intelligence Chatbot Disclosure on Customer Purchases," Marketing Science, INFORMS, vol. 38(6), pages 937-947, November.
    4. Mahmud, Hasan & Islam, A.K.M. Najmul & Ahmed, Syed Ishtiaque & Smolander, Kari, 2022. "What influences algorithmic decision-making? A systematic literature review on algorithm aversion," Technological Forecasting and Social Change, Elsevier, vol. 175(C).
    5. Siliang Tong & Nan Jia & Xueming Luo & Zheng Fang, 2021. "The Janus face of artificial intelligence feedback: Deployment versus disclosure effects on employee performance," Strategic Management Journal, Wiley Blackwell, vol. 42(9), pages 1600-1631, September.
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