Copyright Protection and AI-Generated Works-A Fight We Have Already Lost?

Research output: Contribution to journalArticleScientificpeer-review


The use of AI in the creative arts has become so sophisticated that computer-generated work is difficult to distinguish from purely human-created work. Such development raises questions over whether AI-generated works are copyright pro-tected or not, and who should own the copyright: the AI itself, the maker of the AI system, the user of the AI system, or no-one. The paper explores the two main normative arguments against according copyright protection to AI-generated works: firstly, that a work must be created by a natural person and that thus AI-generated works cannot be protected; and secondly, that many AI-generated works lack originality. Whether a work is generated solely by an AI or created with assistance from an AI, there is a good chance that it will lack originality, meaning that many AI-generated works should not be protected by copyright. However, the paper outlines the position that AI-generated works will get copyright protection to some extent, despite the normative arguments against this. The paper identifies three main reasons why AI-generated works will get protection: the difficulty of telling AI-generated and human-authored works apart; the economic, political, and practical arguments in favour of the protection of AI works; and finally, the legal uncertainty surrounding AI-generated works, which supports their protection. These points challenge both of the normative arguments against giving protection to AI-generated works. There is also a possibility that if AI-generated works are not protected by copyright, rightsholders will turn to alternative protection measures, such as private ordering and changing business methods, which will extend the protection offered to such works beyond copyright’s normal scope.
Original languageEnglish
Trade journalAIDA : Annali italiani del diritto d'autore, della cultura e dello spettacolo
Publication statusPublished - 2020
MoE publication typeA1 Journal article - refereed


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