This is the final part of our four-part series on the EUIPO study on GenAI and copyright. Read parts 1, 2, and 3.
The EUIPO study provides detailed insights into the evolving relationship between GenAI and copyright law, highlighting both the complex challenges and emerging solutions in this rapidly developing field. As discussed in the previous parts of this series, the study addresses crucial issues at both the training (input) and deployment (output) stages of GenAI systems.
This is the third part of our four-part series on the EUIPO study on GenAI and copyright. Read parts 1, 2, and 4.
This third part of the four-part series offers four key takeaways on GenAI output, highlighting critical issues around retrieval augmented generation (RAG), transparency solutions, copyright retention concerns and emerging technical remedies.
This is the second part of our four-part series on the EUIPO study on GenAI and copyright. Read parts 1, 3, and 4.
In this second part of our four-part series exploring the EUIPO study on GenAI and copyright, we set out our key takeaways regarding GenAI inputs, including findings on the evolving interpretation of the legal text and data mining (TDM) rights reservation regime and existing opt-out measures.
This is the first part of four-part series on the EUIPO study on GenAI and copyright. See here for
This third part of our four-part series on using synthetic data to train AI models explores the interplay between synthetic data training sets, the EU Copyright Directive and the forthcoming EU AI Act.
This second part of our four-part series on using synthetic data to train AI models explores how the use of synthetic data training sets may mitigate copyright infringement risks under EU law.
This is the first part of series on using synthetic data to train AI models. See here for Parts 2, 3, and 4.
The recent rapid advancements of Artificial Intelligence (“AI”) have revolutionized creation and learning patterns. Generative AI (“GenAI”) systems have unveiled unprecedented capabilities, pushing the boundaries of what we thought possible. Yet, beneath the surface of the transformative potential of AI lies a complex legal web of intellectual property (“IP”) risks, particularly concerning the use of “real-world” training data, which may lead to alleged infringement of third-party IP rights if AI training data is not appropriately sourced.