Governance of Generative AI in Creative Work: Consent, Credit, Compensation, and Beyond

TL;DR

The paper investigates governance of generative AI in creative work through 20 interviews across visual art/design, writing, and programming, highlighting that the commonly cited 3 Cs—consent, credit, and compensation—yield nuanced benefits and risks in practice. It shows a broad gap between worker needs and current governance at company, platform, and policy levels, and argues for proactive, worker-inclusive regulation that extends beyond the 3 Cs to address data provenance, labeling, and power dynamics. The study provides concrete recommendations for how organizations and regulators can implement consent, credit, and compensation, while also suggesting broader governance principles and future research directions to protect creatives. Overall, the work emphasizes democratizing AI governance to rebalance power, reduce harms, and foster responsible use of generative AI in creative professions.

Abstract

Since the emergence of generative AI, creative workers have spoken up about the career-based harms they have experienced arising from this new technology. A common theme in these accounts of harm is that generative AI models are trained on workers' creative output without their consent and without giving credit or compensation to the original creators. This paper reports findings from 20 interviews with creative workers in three domains: visual art and design, writing, and programming. We investigate the gaps between current AI governance strategies, what creative workers want out of generative AI governance, and the nuanced role of creative workers' consent, compensation and credit for training AI models on their work. Finally, we make recommendations for how generative AI can be governed and how operators of generative AI systems might more ethically train models on creative output in the future.