Challenges posed by Artificial Intelligence to traditional Copyright law: Can Machine Learning meet and disrupt the test of originality?

Abstract

The dissertation first discusses the Test of Originality in Copyright law. While Section 9(1) of the Copyrights, Designs, and Patents Act 1988 lists effort, skill, and labour as being central for creating the new work as core elements for determining the copyrightable aspects of the work, caselaw in the UK has also emphasised on the element of creative aspect of the work. In the context of AI works, it is possible to argue that AI is capable of creativity in terms of traits or behaviours that are generally related to the concept of ‘intelligence’ and are also increasingly seen in the AI technology. This has potential to disrupt the copyright law as it is positioned now. The test of originality, which is an essential criterion for assessment of the copyright protection, is then discussed in the context of works that can be produced with Machine Learning technology. The test of originality requires the assessment of the ‘author’s own intellectual creation’, which has implications for AI works. For example, originality is explained in terms of the personality of the creator in the EU law. On the other hand, in the UK, the Copyright, Designs and Patent Act 1988 creates a different perspective to machine generated content even if there is no human author of the same because the UK law allows person arranging the database to be considered as the author. The UK law also applies a de minimis rule so that the threshold or standard is low with regard to the extent of the effort, skill and labour required for assessing originality of the work. This dissertation argues that the minimum threshold of originality may depend on the type of works, so that the quantitative and qualitative labour becomes important for considering the applicability of copyright. It may be noted that qualitative requirements where applicable, may be difficult to prove for AI works since it is easy to identify the creative choices made by AI because of the process of data analysis and processing. This is not the same as human authors whose creative choices can be individualistic and unique. With regard to Section 9, the dissertation argues that it does not answer to all the range of works that can be created by Machine Learning since it is possible to develop works via Machine Learning without any explicit programming since it can learn from their past experiences. This may obliterate the need for a person who makes the arrangements for the work who is only active during initial programming. In such a situation, the test of originality may be relevant to determining the extent to which copyright may be applicable even if the person who made the arrangements was only active at the beginning of the programming. The nature of the work may also be relevant since while the works of literary nature can be done by the AI in a way that is original in the sense of skill and labour and investment but the same cannot be said of visual arts, where a question may be raised about the artistic choices that the AI may not be able to meet. However, it is important to identify the range of works that can be done by the AI and then also provide framework under copyright law that addresses the creativity of the Machine Learning since suitability of the traditional copyright law may be raised in future when the commercial interests of the companies or investors in AI technologies come in conflict with the legal gaps in copyright law.