🔍 In this TL;DR episode, Joan explains how Article 50 of the EU AI Act sets out high-level transparency obligations for AI developers and deployers—requiring users to be informed when they interact with AI or access AI-generated content—while noting that excessive labeling can itself be misleading. She highlights why the forthcoming Code of Practice must focus on clear principles rather than fixed technical solutions, ensuring transparency helps prevent deception without creating confusion in a rapidly evolving technological environment.

📌 TL;DR Highlights

⏲️[00:00] Intro

⏲️[00:33] Q1-What’s the core purpose of Article 50, and why is this 10-month drafting window so critical for the industry?

⏲️[02:31] Q2-What’s the difference between disclosing a chatbot and technically marking AI-generated media?

⏲️[06:27] Q3-What is the inherent danger of "too much transparency" or over-labeling content? How do we prevent the "liar's dividend" and "label fatigue" while still fighting deception?

⏲️[10:00] Q4-If drafters should avoid one rigid technical fix, what’s your top advice for building flexibility into the Code of Practice?

⏲️[13:11] Q5-Did you consult other stakeholders when developing your whitepaper analysis?

⏲️[16:45] Wrap-up & Outro

💭 Q1 - What’s the core purpose of Article 50, and why is this 10-month drafting window so critical for the industry?

🗣️ “Article 50 sets only broad transparency rules—so a strong Code of Practice is essential.”

💭 Q2 - What’s the difference between disclosing a chatbot and technically marking AI-generated media?

🗣️ “If there’s a risk of confusion, users must be clearly told they’re interacting with AI.”

💭 Q3 - What is the inherent danger of "too much transparency" or over-labeling content? How do we prevent the "liar's dividend" and "label fatigue" while still fighting deception?

🗣️ “Too much transparency can mislead just as much as too little.”

💭 Q4 - If drafters should avoid one rigid technical fix, what’s your top advice for building flexibility into the Code of Practice?

🗣️ “We should focus on principles, not chase technical solutions that will be outdated in months.”

💭 Q5 - What is the one core idea you want policymakers to take away from your research?

🗣️ “Transparency raises legal, technical, psychological, and even philosophical questions—information alone doesn’t guarantee real agency."

📌 About Our Guests

🎙️ Joan Barata | Faculdade de Direito - Católica no Porto

🌐 linkedin.com/in/joan-barata-a649876

Joan Barata works on freedom of expression, media regulation, and intermediary liability issues. He is a Visiting professor at Faculdade de Direito - Católica no Porto and Senior Legal Fellow at The Future Free Speech project at Vanderbilt University. He is also a Fellow of the Program on Platform Regulation at the Stanford Cyber Policy Center.

#AI #artificialintelligence #generativeAI

🔍 In this episode, Caroline and Alene unravel why the popular idea of “AI memorisation” leads policymakers down the wrong path—and how this metaphor obscures what actually happens inside large language models. Moving from the technical realities of parameter optimisation to the policy dangers of doctrinal drift, they explore how misleading language can distort copyright debates, inflate compliance burdens, and threaten Europe’s research and innovation ecosystem.

📌 TL;DR Highlights

⏲️[00:00] Intro

⏲️[00:33] Q1-In your view, what is the biggest misunderstanding behind the ‘memorisation’ metaphor in AI, and why is this framing so problematic when applied to copyright law?

⏲️[02:00] Q2-what actually happens inside a large language model during training, and explain why this process should not be treated as copyright ‘reproduction’?

⏲️[03:32] Q3-What do you see as the main legal, economic, and innovation risks for Europe if policymakers continue relying on the memorisation metaphor when designing AI regulation?

⏲️[04:39] Q4-If ‘memorisation’ is the wrong frame, what alternative concepts or policy focus areas should policymakers adopt to regulate AI more accurately and effectively?

⏲️[06:28] Q5-What is the one core idea you want policymakers to take away from your research?

⏲️[07:32] Wrap-up & Outro

💭 Q1 - In your view, what is the biggest misunderstanding behind the ‘memorisation’ metaphor in AI, and why is this framing so problematic when applied to copyright law?

🗣️ “A large language model is not a filing cabinet full of copyrighted material.”

💭 Q2 - what actually happens inside a large language model during training, and explain why this process should not be treated as copyright ‘reproduction’?

🗣️ "Training is parameter optimisation, not the storage of protected expression.”

💭 Q3 - What do you see as the main legal, economic, and innovation risks for Europe if policymakers continue relying on the memorisation metaphor when designing AI regulation?

🗣️ "Stretching the reproduction right to cover statistical learning would be disastrous for research and innovation in Europe.”

💭 Q4 - If ‘memorisation’ is the wrong frame, what alternative concepts or policy focus areas should policymakers adopt to regulate AI more accurately and effectively?

🗣️ "We need mechanism-aware regulation, not metaphor-driven lawmaking.”

💭 Q5 - What is the one core idea you want policymakers to take away from your research?

🗣️ "Don’t write rules for filing cabinets when we are dealing with statistical models.”

📌 About Our Guests

🎙️ Aline Larroyed | Dublin City University

🌐 linkedin.com/in/aline-l-624a3655

🌐 Article | The Fallacy Of The File: How The Memorisation Metaphor Misguides Copyright Law And Stifles AI Innovation

https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5782882

Aline Larroyed is a postdoctoral researcher at Dublin City University and holds a PhD in International Law with a background in linguistics. She brings 20 years of experience in human rights, intellectual property, and international regulation, and is a member of the Institute for Globalization and International Regulation at Maastricht University and the COST LITHME network.

#AI #ArtificialIntelligence #GenerativeAI