The Artificial Intelligence Disclosure Penalty: Humans Persistently Devalue AI-Generated Creative Writing
Abstract
Although preliminary evidence suggests that humans often react aversely to artificial intelligence (AI)-generated creative works, we have little understanding of how robust or persistent these reactions may be. In a series of 16 preregistered experiments (N = 27,491), we examine how evaluations of creative writing are affected by whether participants believe the content is produced with an AI model. We find consistent evidence of an AI disclosure penalty: Participant evaluations of creative writing decrease when they believe writing samples were written by an AI model—or with the help of one—rather than a human author alone, and this effect is mediated by perceived authenticity. The AI disclosure penalty is sticky, persisting across evaluation metrics, contexts, kinds of written content, and multiple interventions derived from prior research aimed at moderating the effect. Our results indicate that AI disclosure penalties about creative writing may be stubbornly difficult to mitigate, at least at this time.
Study specs
- Institution
- New York University,University of Michigan,Wharton
- Discipline
- Experimental psychology
- Year
- 2026
- Human Data Platform
- Prolific
- Source
- View Source DOI Google Scholar
Peer Review & Critical Discussion
Potential Selection Bias in 2023 Cohort
The participant pool shows a concerning overrepresentation of users from high-income demographics. Looking at Table 3, we can see that 78% of respondents had annual incomes above $75k, which significantly limits the generalizability of these findings to broader populations.
Non-naive Participants Issue
I've noticed a methodological concern regarding participant naivety. Given that Prolific users often complete multiple studies, there's a real risk that participants had prior exposure to similar experimental paradigms, which could confound the results.
RLHF Applicability to This Study Design
The implications for RLHF training pipelines are understated. If we accept the authors' conclusions about preference stability, this has direct consequences for how we should structure reward model training. The temporal decay effect described in Section 4.2 is particularly relevant.
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