Algorithmic Copyright Enforcement and AI: Issues and Potential Solutions through the Lens of Text and Data Mining
Although digitalization and the emergence of the Internet has caused a long-term crisis for copyright law, technology itself also seems to offer a seemingly ideal solution to the challenges of digital age: copyright has been a major use case for algorithmic enforcement from the early days of digital rights management technologies to the more advanced content recognition algorithms. These technologies identify and filter possibly infringing content automatically, effectively and often in a preventive fashion. These methods have been criticized for their shortcomings, such as the lack of transparency, bias and the possible impairment of fundamental rights. Self-learning machines and semi-autonomous AI have the potential to offer even more sophisticated and expeditious enforcement by code, however, they could also aggravate the aforementioned issues. As the EU legislator envisions to make the use of such technologies essentially obligatory for certain online content sharing service providers (via the infamous Article 17 of the directive on copyright in the digital single market), the assessment of the situation in light of future technological development has become a current topic.
This paper aims to identify the main issues and potential long-term consequences of creating legislation that practically requires the employment of such filtering algorithms as well as their solutions. This paper focuses on the potential role a broad copyright exception for text and data mining could play in counterbalancing the issues associated with algorithmic enforcement.
AI, Copyright Law, EU Law, Machine Learning, Technology, Text and Data Mining
Andrea Katalin Tóth
Hungarian Intellectual Property Office Eötvös Loránd University Faculty of Law and Political Science
Legal officer at the International Copyright Law at the Hungarian Intellectual Property Office
Ph.D. candidate at the Eötvös Loránd University Faculty of Law and Political Science
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