ABSTRACT
Copyright enforcement has become one of the most contested and consequential aspects of algorithmic content moderation. While online platforms increasingly deploy automated systems to identify and manage infringing material, these mechanisms often lack contextual nuance, over-prioritise corporate rightsholders and suppress lawful content such as transformative works and fair use. YouTube’s Content ID exemplifies how copyright-focused moderation reshapes digital governance, raising concerns over bias, opacity and limited procedural safeguards. This article critically examines the copyright implications of algorithmic enforcement by comparing United States and European Union legal frameworks, with a detailed case study of Content ID. To address these shortcomings, the study proposes data trusts as a governance model capable of ensuring fairer and more transparent copyright enforcement. By shifting oversight from private platforms to independent fiduciary entities, data trusts may improve enforcement accuracy while protecting users’ expressive rights. Given the implementation challenges, including legal uncertainty and institutional resistance, the article explores regulatory sandboxes as a structured pathway to test and refine data trust models before legislative adoption. By integrating copyright law, regulatory design and platform governance theory, this article contributes to ongoing efforts to create rights-respecting, economically viable alternatives to platform-dominated enforcement systems. It calls for a proactive, evidence-based shift towards fairer digital governance that protects both intellectual property and fundamental freedoms.
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Laura Filliung, Algorithmic challenges in online content moderation: exploring human rights and copyright issues and proposing data trusts as a policy solution, Queen Mary Journal of Intellectual Property volume 16 issue 1 pp 28-50. Published: 1 April 2026.
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