Yes, But Not Always. Generative AI Needs Nuanced Opt-in

2026-04-10

AI Summary

Current generative AI systems rely on coarse-grained, one-size-fits-all authorization mechanisms that struggle to accommodate complex copyright structures, stylistic imitation, and diverse usage scenarios, thereby undermining traditional binary consent models. This work proposes integrating fine-grained control into generative AI workflows by focusing on conditional authorization verification during the inference phase, positioning inference-time authorization as a pivotal lever for precise copyright governance. We introduce an agent-based architecture that dynamically validates user requests against rights holders’ specified licensing rules and implement this approach grounded in real-world music rights management practices. Experimental results demonstrate that our method effectively aligns with existing copyright frameworks, reestablishes a balance of power between developers and rights holders, and offers a viable pathway toward compliant deployment of generative AI technologies.

Technology Category

• Multiagent Systems: Mechanism Design
• Natural Language Processing: Generation
• Philosophy and Ethics of AI: Artificial General Intelligence

Application Category

• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems

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.

Problem

• generative AI
• consent
• opt-in
• copyright
• nuanced consent

Innovation

• nuanced consent
• inference-time opt-in
• generative AI
• rights management
• agent-based architecture