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AI Bias for Creative Writing: Subjective Assessment Versus Willingness to Pay

Author

Listed:
  • Abel, Martin

    (Bowdoin College)

  • Johnson, Reed

    (Bowdoin College)

Abstract

How do perceptions of AI versus human authorship affect engagement with creative work? In an incentivized experiment, participants (N=654) assessed the content of a short story labeled as either human or AI-generated and reported their willingness to pay and work to finish reading it. Consistent with prior research, the AI-labeled story received significantly lower content assessments. However, the time people invest in reading the story and their willingness to pay and work did not differ between the labels, even for the 36% of participants who profess to value human over AI writing. These findings raise questions about whether subjective assessments and aspirations to favor human authorship translate into actions.

Suggested Citation

  • Abel, Martin & Johnson, Reed, 2025. "AI Bias for Creative Writing: Subjective Assessment Versus Willingness to Pay," IZA Discussion Papers 17646, Institute of Labor Economics (IZA).
  • Handle: RePEc:iza:izadps:dp17646
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    References listed on IDEAS

    as
    1. Nina Beguš, 2024. "Experimental narratives: A comparison of human crowdsourced storytelling and AI storytelling," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-22, December.
    2. James Murphy & P. Allen & Thomas Stevens & Darryl Weatherhead, 2005. "A Meta-analysis of Hypothetical Bias in Stated Preference Valuation," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 30(3), pages 313-325, March.
    3. Jerrod M Penn & Wuyang Hu, 2018. "Understanding Hypothetical Bias: An Enhanced Meta-Analysis," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 100(4), pages 1186-1206.
    4. Ned Augenblick & Muriel Niederle & Charles Sprenger, 2015. "Editor's Choice Working over Time: Dynamic Inconsistency in Real Effort Tasks," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 130(3), pages 1067-1115.
    5. Roland Bénabou & Jean Tirole, 2003. "Intrinsic and Extrinsic Motivation," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 70(3), pages 489-520.
    6. Kreps, Sarah & McCain, R. Miles & Brundage, Miles, 2022. "All the News That’s Fit to Fabricate: AI-Generated Text as a Tool of Media Misinformation," Journal of Experimental Political Science, Cambridge University Press, vol. 9(1), pages 104-117, March.
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    More about this item

    Keywords

    Artificial Intelligence; creative writing; willingness to pay; AI bias;
    All these keywords.

    JEL classification:

    • D12 - Microeconomics - - Household Behavior - - - Consumer Economics: Empirical Analysis
    • O33 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Technological Change: Choices and Consequences; Diffusion Processes
    • Z11 - Other Special Topics - - Cultural Economics - - - Economics of the Arts and Literature

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