🔍 In this TL;DR episode, Professor Thomas Margoni (CiTiP - Centre for IT & IP Law, KU Leuven) discusses copyright law and the lifecycle of machine learning models with the AI lab. The starting point is an article co-authored with Professor Martin Kretschmer (CREATe, University of Glasgow) and Dr Pinar Oruç (University of Manchester), and published in open access in the International Review of Intellectual Property and Competition Law (IIC).

📌 TL;DR Highlights

⏲️[00:00] Intro

⏲️[01:26] Q1-Copyright & training data:

How does current copyright law affect the training of machine learning models?

What insights do your case studies provide? 

⏲️[04:57] Q2-Surprising research findings:

What did you learn about copyright law’s impact on machine learning innovation?

⏲️[08:16] Q3-Policy recommendations:

What changes to copyright law do you suggest to support machine learning development and research?

⏲️[12:50] Wrap-up & Outro

💭 Q1 - Copyright & Training Data

🗣️ It is a complex relationship: machine learning is a very new technology, and copyright is a very old law (...) developed (...) in function of a very different (...) technology.

🗣️ Every time a new technology appears (...), adjustment [of copyright law] is necessary. During this time (...) various interests [and] dynamics are at play.

🗣️ A third interest that is naturally underrepresented (...) is that of users, citizens, people like us, who somehow get lost in this equation based on only two players[: right holders and AI developers].

🗣️ Copyright has always been about the balance between authors and the public[,] between the need to incentivise cultural creation and the need for the public to have access to it.

💭 Q2 - Surprising Research Findings

🗣️ Be careful not to treat different cases following the same rules (...) [it] would lead to unbalanced solutions. (...) Different cases (...) are [now] treated almost entirely the same by EU copyright law.

🗣️ Text and data mining: (...) could lead to identifying (...) the spread of a pandemic (...) This is a public-interest form of learning that can benefit the entire humanity. This type of activity should not be regulated by copyright.

💭 Q3 - Policy Recommendations

🗣️ The EU (...) developed a legal framework whereby text and data mining and machine learning are regulated the same. (...) Perhaps one of the answers (...) to creat[e] more (...) breathing space, particularly for scientific research, is to treat them differently.

🗣️ The protection of research, freedom of scientific research and artistic expression are very important. (...) We have to design rules that do not prevent scientists [and] citizens (...) to experiment with these tools.

🗣️ Right now, we regulate everything at the input level. (...) We have to move our regulatory focus: look more at the input and output data.

🗣️ Due to the scale of AI applications, there is a danger raised by rightholders and some artists [of a] substitution effect (...) with a specific artist, school or genre. This (...) is a (...) new question, and (...) remuneration models (...) could be an (...) avenue to explore.

📌 About Our Guest

🎙️ Professor Thomas Margoni | Research Professor of IP Law at the Faculty of Law and Criminology and member of the Board of Directors of the Centre for IT & IP Law (CiTiP), KU Leuven

🌐 International Review of IP & Competition Law (IIC) - Copyright Law and the Lifecycle of Machine Learning Models

https://doi.org/10.1007/s40319-023-01419-3 

🌐 Prof. Thomas Margoni

https://www.law.kuleuven.be/citip/en/staff-members/staff/00137042

Dr Thomas Margoni is a Research Professor of Intellectual Property Law at the Faculty of Law and Criminology of KU Leuven in Belgium. He is also a member of the Board of Directors of the Centre for IT & IP Law (CiTiP, KU Leuven).