Ownership and Creativity in Generative Models

TL;DR

This paper explores ownership issues in AI-generated content, proposing a potential algorithmic solution and discussing broader implications for creators, users, and legal frameworks.

Contribution

It introduces a new algorithmic approach for ownership determination in vision-based generative models and discusses the broader impact of ownership questions.

Findings

Proposes a novel algorithmic solution for ownership in generative models.

Analyzes different ownership candidates and their implications.

Discusses broader societal and legal impacts of AI-generated content.

Abstract

Machine learning generated content such as image artworks, textual poems and music become prominent in recent years. These tools attract much attention from the media, artists, researchers, and investors. Because these tools are data-driven, they are inherently different than the traditional creative tools which arises the question - who may own the content that is generated by these tools? In this paper we aim to address this question, we start by providing a background to this problem, raising several candidates that may own the content and arguments for each one of them. Then we propose a possible algorithmic solution in the vision-based model's regime. Finally, we discuss the broader implications of this problem.