Yes, But Not Always. Generative AI Needs Nuanced Opt-in
2026-04-10 • Computers and Society
Computers and SocietyArtificial Intelligence
AI summaryⓘ
The authors say that simply asking for permission once at the start to use creative works in AI is not enough because ownership and context vary widely. They explore different moments in the AI process—training, using, and sharing—and suggest that asking for permission right when the AI is being used (inference time) is a good way to manage consent more carefully. They propose a system where AI checks if a user's request follows the rules set by rights holders before proceeding. Using music as an example, the authors show that this approach can respect rights holders’ wishes and balance control between them and AI developers.
generative AIconsentinference timerights holdersopt-increative worksAI trainingmusic rightsagent-based architecturenuanced consent
Authors
Wiebke Hutiri, Morgan Scheuerman, Shruti Nagpal, Austin Hoag, Alice Xiang
Abstract
This paper argues that a one-size-fits-all approach to specifying consent for the use of creative works in generative AI is insufficient. Real-world ownership and rights holder structures, the imitation of artistic styles and likeness, and the limitless contexts of use of AI outputs make the status quo of binary consent with opt-in by default untenable. To move beyond the current impasse, we consider levers of control in generative AI workflows at training, inference, and dissemination. Based on these insights, we position inference-time opt-in as an overlooked opportunity for nuanced consent verification. We conceptualize nuanced consent conditions for opt-in and propose an agent-based inference-time opt-in architecture to verify if user intent requests meet conditional consent granted by rights holders. In a case study for music, we demonstrate that nuanced opt-in at inference can account for established rights and re-establish a balance of power between rights holders and AI developers.