Rethinking Copyright Legitimacy:AI-Generated Content and Distributive Justice under the Instrumental Approach

Abstract

The proliferation of AI-generated content (over15 billion algorithmically created images in 2022–2023) has destabilized traditional copyright frameworks rooted in the labor theory of property, the personality theory, and the incentive theory. These theories fail to address AI’s disruption of human algorithm co-creation. This study adopts the instrumental approach, reconceptualizing copyright as a governance tool to promote knowledge sharing and public welfare. Through legal-philosophical critiques, the paper exposes historical flaws in classic theories, including their anthropocentric biases and systemic erosion of the public domain. Comparative analysis of international cases (eg, US, China, EU) reveals divergent judicial standards for AI generated content. By integrating three principles — the principle of attribution to organizers (rights allocation), prioritizing the least advantaged stakeholders (distributive justice), and safeguarding the public domain (dynamic protection terms) — the study proposes a practice-oriented regulatory pathway to balance innovation incentives, equitable access, and sustainable knowledge ecosystems in the AI era.