The Indirect Disclosure Effect: How Disclosing Generative AI Use Impacts Human Creative Collaboration with AI

Abstract

Regulators increasingly mandate transparency regarding generative AI (GenAI) use in creative work, aiming to protect audiences from deception while preserving creators' self-expression. One way of achieving this transparency is through disclosure labels that directly inform audiences about GenAI use. Yet, prior research focused almost exclusively on how such labels affect audience evaluations and paid surprisingly little attention to whether mandatory disclosure affects creators, too. We refer to this as the indirect disclosure effect. Drawing on Goffman's account of impression management, we theorize that creators who anticipate disclosure fear that audiences will not recognize their human creative agency, threatening their validation as a creative self, which leads them to adjust their collaboration with GenAI. To investigate this mechanism, we employ two nested mixed-methods experiments in which participants collaborate with a text-to-image GenAI tool under different disclosure conditions. We empirically establish the indirect disclosure effect: when disclosure is anticipated, the majority of creators withdraw from the creative process, leaving image generation to GenAI. We provide evidence that this withdrawal is driven by creators’ fears that audiences will not recognize their creative agency. Hence, the produced artifacts predominantly reflect computational rather than human creativity, which is also recognized and evaluated by the audience regardless of the direct disclosure label. Overall, our study reveals a fundamental tension at the heart of transparency regulation: by disclosing GenAI use through a simple label, regulators may inadvertently diminish the very human creative agency they aim to protect, and they do so before audiences ever see the label.