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

arXiv: https://arxiv.org/abs/2604.09413 · HTML: https://mlsanity.com/paper/2604.09413/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 84 references that are in the map.
- First posted on arXiv 10 Apr 2026.

## Builds on (most cited first, 15 of 15)

- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [DataComp: In search of the next generation of multimodal datasets](https://mlsanity.com/paper/2304.14108/) (2023)
- [Homogenization Effects of Large Language Models on Human Creative Ideation](https://mlsanity.com/paper/2402.01536/) (2024)
- [Consent in Crisis: The Rapid Decline of the AI Data Commons](https://mlsanity.com/paper/2407.14933/) (2024)
- [The Stronger the Diffusion Model, the Easier the Backdoor: Data Poisoning to Induce Copyright Breaches Without Adjusting Finetuning Pipeline](https://mlsanity.com/paper/2401.04136/) (2024)
- [Automatic Jailbreaking of the Text-to-Image Generative AI Systems](https://mlsanity.com/paper/2405.16567/) (2024)
- [Not My Voice! A Taxonomy of Ethical and Safety Harms of Speech Generators](https://mlsanity.com/paper/2402.01708/) (2024)
- [Governance of Generative AI in Creative Work: Consent, Credit, Compensation, and Beyond](https://mlsanity.com/paper/2501.11457/) (2025)
- [Supervised contrastive learning from weakly-labeled audio segments for musical version matching](https://mlsanity.com/paper/2502.16936/) (2025)
- [A Survey of Web Content Control for Generative AI](https://mlsanity.com/paper/2404.02309/) (2024)
- [Missing Melodies: AI Music Generation and its "Nearly" Complete Omission of the Global South](https://mlsanity.com/paper/2412.04100/) (2024)
- [Attribution-by-design: Ensuring Inference-Time Provenance in Generative Music Systems](https://mlsanity.com/paper/2510.08062/) (2025)
- [From Generation to Attribution: Music AI Agent Architectures for the Post-Streaming Era](https://mlsanity.com/paper/2510.20276/) (2025)
- [ai.txt: A Domain-Specific Language for Guiding AI Interactions with the Internet](https://mlsanity.com/paper/2505.07834/) (2025)
- [How Do Data Owners Say No? A Case Study of Data Consent Mechanisms in Web-Scraped Vision-Language AI Training Datasets](https://mlsanity.com/paper/2511.08637/) (2025)

## Abstract (arXiv)

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.