生成式AI需精细化授权，避免一刀切同意机制。
Yes, But Not Always. Generative AI Needs Nuanced Opt-in
- 在推理阶段引入条件化同意机制，动态验证用户意图。
- 案例显示该方案可平衡创作者与开发者权益。
- 适合关注版权合规与AI伦理的研究者与从业者。
本文指出，对创意作品用于生成式AI采用统一的二元同意机制（默认同意）已不可行。现实中的权属结构、艺术风格模仿、以及AI输出的无限应用场景，使当前模式难以持续。为突破僵局，论文从训练、推理、分发三个环节探讨控制手段，提出将推理阶段的同意机制作为被忽视的突破口。通过构建基于代理的推理阶段同意架构，实现对用户请求是否符合权利人设定条件的验证。以音乐领域为例，证明推理阶段的精细化同意能兼顾既有版权规则，重置创作者与开发者的权力平衡。
原文摘要 · Abstract (English)
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.
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