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

TL;DR

This paper advocates for nuanced, inference-time opt-in consent mechanisms in generative AI to better respect rights holders' conditions, moving beyond binary consent models.

Contribution

It introduces an agent-based inference-time opt-in architecture for verifying nuanced consent conditions in generative AI workflows.

Findings

Inference-time opt-in can effectively verify user intent against rights holders' conditions.

The proposed architecture rebalances power between rights holders and AI developers.

Case study in music demonstrates practical applicability of nuanced consent verification.

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.