AI, Authorship, Copyright, and Human Originality

Neubauer, Anja, Wynn, Martin G ORCID: https://orcid.org/0000-0001-7619-6079 and Bown, G Robin ORCID: https://orcid.org/0000-0001-7793-108X (2026) AI, Authorship, Copyright, and Human Originality. Encyclopedia, 6 (1). pp. 1-16. doi:10.3390/encyclopedia6010009

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Abstract

This entry explores the implications of generative AI for the underlying foundational premises of copyright law and the potential threat it poses to human creativity. It identifies the gaps and inconsistencies in legal frameworks as regards authorship, training-data use, moral rights and human originality in the context of AI systems that are capable of imitating human expression at both syntactic and semantic levels. The entry includes (i) a comparative analysis of the legal frameworks of the United Kingdom, United States and Germany, using the Berne Convention as a harmonizing baseline, (ii) a systematic synthesis of the relevant academic literature and (iii) in-sights gained from semi-structured interviews with legal scholars, AI developers, industry stakeholders and creators. Evidence suggests that existing laws are ill-equipped for semantic and stylistic reproduction: there is no agreement on authorship, no clear licensing model for training data, and inadequate protection for the moral identity of creators - especially posthumously, where explicit protections for likeness, voice and style are fragmented. The entry puts forward a draft global framework to restore legal certainty and cultural value, incorporating a seman-tics-aware definition of the term "work", and encompassing licensing and remuneration of training data, enhanced moral and posthumous rights as well as enforceable transparency. At the same time, parallel personality-based safeguards, including rights of publicity, image or likeness, although present in all three jurisdictions studied, are not subject to the same copyright and thus do not offer any coherent or adequate protection against semantic or stylistic imitation, which once again highlights the need for a more unified and robust copyright strategy.

Item Type:	Article
Article Type:	Article
Uncontrolled Keywords:	Artificial intelligence; AI; Copyright; Moral rights; Authorship; Training data; Human originality; Transparency; Global framework
Related URLs:	Publisher
Subjects:	Q Science > Q Science (General) > Q336 Artificial intelligence Q Science > QA Mathematics > QA75 Electronic computers. Computer science T Technology > T Technology (General)
Divisions:	Schools and Research Institutes > School of Business, Computing and Social Sciences
Depositing User:	Martin Wynn
Date Deposited:	06 Jan 2026 14:30
Last Modified:	07 Jan 2026 12:00
URI:	https://eprints.glos.ac.uk/id/eprint/15715

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