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.

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Research
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Study specs

Year
2026
Human Data Platform
Prolific

Peer Review & Critical Discussion

3 threads

Potential Selection Bias in 2023 Cohort

DSJDr. Sarah J.
Verified PhD Candidate
12 replies

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.

2 hours ago

Non-naive Participants Issue

MCM. Chen (OpenAI)
Data Scientist
8 replies

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.

5 hours ago

RLHF Applicability to This Study Design

PRWProf. R. Williams
Verified Researcher
15 replies

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.

1 day ago

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