Examining generative image models amidst privacy regulations

Abstract

Abstract As diffusion models emerge as a new frontier in generative AI, requiring vast image databases as their inputs, the question arises: how should regulators approach policies concerning the collection and utilization of these images? Though generative image models currently interpret the data they scrape as public, regulatory bodies have yet to confirm this as a viable understanding. This paper explores the current public/personal distinction of data as well as the respective legal standards for both categories in both the American and European context. This paper acts as a guide for regulators seeking to understand monopolization and privacy implications of confirming the validity of using open sourced images versus imagining a reality of curated or licensed datasets amidst outrage from artists over a breach of an expectation of collection/use to their artwork. Though arguments have been made regarding using copyright to protect artists, this paper seeks to explore other pathways for regulating generative image models under our current conceptual frameworks of privacy.