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WP3 – Authors and performers

Digitisation, the Internet and more recently AI have been disruptive forces that brought new opportunities and challenges for authors and performers. Never before has it been so easy for creators and performers – both professionals and amateurs – to reach an almost worldwide audience, yet never before has it been so easy for their audience to obtain content without paying for it. More recently, the emerging role of AI machines as producers of literary and artistic works may pose a threat to human authorship. The balance of these opportunities and threats has so far remained undecided and the position of authors and performers in this is relatively under-researched.

Against this background, this WP has three core elements:

  • In the task Perspective of Creators and performing Artists on Digitization, Copyright and the Digital Single market, a survey will be conducted among creators and performing artists throughout the EU, to learn about their perspectives on and experiences with digitalisation, their experiences with platforms and publishers, their views on copyright and piracy issues, their income developments, (fear of) redundancy by IA driven creation, etc.
  • The task The Growing Role of AI machines as Producers of Literary and Artistic Works: Challenges to Human Authorship aims to identify the role that machines using Artificial Intelligence (AI) algorithms are playing in producing “content” that takes the form of literary and artistic works; to identify the impact that this production has on human creativity and remuneration; and to determine whether productions of AI machines in the literary and artistic sector are, or should be, protected by authors’ rights.
  • The task AI, Machine learning and EU copyright law: ownership issues in training data aims to identify the role played by “training data” in the field of AI and Machine Learning (ML). The task focuses on the “ownership” aspects of AI/ML by looking at the legal status of the training data (literary or artistic works, public domain material, data); at the processes involved in ML algorithms (e.g. at the annotation and enrichment of text) and at the results (the trained models).

 

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