AI lab by information labs

🔍 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 fixe...

🔍 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 complianc...

🔍 In this TL;DR episode, Anna and Nate unpack why calling AI outputs “hallucinations” misses the mark—and introduce “AI Mirage” as a sharper, more accurate metaphor. From scoring alternative terms to sparking social media debates, they show how language shapes our assumptions, trust, and agency in the age of generative AI. The takeaway: choosing the right words is a hopeful act of shaping our AI...

🔍 In this TL;DR episode, Emmie Hine (Yale Digital Ethics Center) makes the case for Europe’s leadership in open-source AI—thanks to strong infrastructure, multilingual data, and regulatory clarity. With six key policy recommendations, the message is clear: trust and transparency can make EU models globally competitive. 📌 TL;DR Highlights ⏲️[00:00] Intro ⏲️[00:43] Q1-What advantages make the EU a...

🔍 In this TL;DR episode, Milton Mueller (the Georgia Institute of Technology School of Public Policy) argues that what we call “AI” is really just part of a broader digital ecosystem. Instead of vague, top-down AI regulation, he calls for context-specific rules that address actual uses—like facial recognition or medical diagnostics—rather than the underlying technology. The key message: regulate...

🔍 In this TL;DR episode, Kevin Frazier (University of Texas at Austin school of Law) outlines a proposal to realign U.S. copyright law with its original goal of spreading knowledge. The discussion introduces three key reforms—an AI training presumption, research safe harbors, and data commons—to help innovators access data more easily. By reducing legal ambiguity, the proposals aim to support res...

🔍 In this TL;DR episode, Paul Keller (The Open Future Foundation) outlines a proposal for a common opt-out vocabulary to improve how EU copyright rules apply to AI training. The discussion introduces three clear use cases—TDM, AI training, and generative AI training—to help rights holders express their preferences more precisely. By standardizing terminology across the value chain, the proposal a...

🔍 In this TL;DR episode, João Quintais (Institute for Information Law) explains the interaction between the AI Act and EU copyright law, focusing on text and data mining (TDM). He unpacks key issues like lawful access, opt-out mechanisms, and transparency obligations for AI developers. João explores challenges such as extraterritoriality and trade secrets, offering insights into how voluntary cod...

🔍 In this TL;DR episode, Anna Tumadóttir (Creative Commons) discusses how the evolution of creator consent and AI has reshaped perspectives on openness, highlighting the challenges of balancing creator choice with the risks of misuse. Examines the limitations of blunt opt-out mechanisms like those in the EU AI Act, the implications for marginalized communities and open access, and explores the ne...

🔍 In this TL;DR episode, Carys J Craig (Osgoode Professional Development) explains the "copyright trap" in AI regulation, where relying on copyright favors corporate interests over creativity. She challenges misconceptions about copying and property rights, showing how this approach harms innovation and access. Carys offers alternative ways to protect human creativity without falling into this tr...

🔍 In this TL;DR episode, Ariadna Matas (Europeana Foundation) discusses how the 2019 Copyright Directive has influenced text and data mining practices in cultural heritage institutions, highlighting the tension between public interest missions and restrictive approaches, and explores the broader implications of opt-outs on access, research, and the role of AI in the cultural sector. 📌 TL;DR High...

🔍 In this TL;DR episode, Martin Senftleben (Institute for Information Law (IViR) & University of Amsterdam) discusses how EU regulations, including the AI Act and copyright frameworks, impose heavy burdens on AI training and development. The discussion highlights concerns about bias, quality, and fairness due to opt-outs and complex rights management systems, questioning whether these rules t...

🔍 In this TL;DR episode, Mark Lemley (Stanford Law School) discusses how generative AI challenges traditional copyright doctrines, such as the idea-expression dichotomy and substantial similarity test, and explores the evolving role of human creativity in the age of AI. 📌 TL;DR Highlights ⏲️[00:00] Intro ⏲️[00:54] Q1-How does genAI challenge traditional copyright doctrines and will this lead to...

🔍 In this TL;DR episode, Jacob Mchangama (The Future of Free Speech & Vanderbilt University) discusses the high rate of AI chatbot refusals to generate content for controversial prompts, examining how this may conflict with the principles of free speech and access to diverse information. 📌 TL;DR Highlights ⏲️[00:00] Intro ⏲️[00:51] Q1-How does the high rate of refusal by chatbots to generate...

🔍 In this TL;DR episode, Jurgen Gravestein (Conversation Design Institute) discusses his Substack blog post delving into the ‘Intelligence Paradox’ with the AI lab 📌 TL;DR Highlights ⏲️[00:00] Intro ⏲️[01:08] Q1-The ‘Intelligence Paradox’: How does the language used to describe AI lead to misconceptions and the so-called ‘Intelligence Paradox’? ⏲️[05:36] Q2-‘Conceptual Borrowing’: What is ‘conce...

🔍 In this TL;DR episode, Dr. Stefaan G. Verhulst (The GovLab & The Data Tank) discusses his Frontiers Policy Labs contribution on the urgent need to preserve data access for the public interest with the AI lab 📌 TL;DR Highlights ⏲️[00:00] Intro ⏲️[01:13] Q1-‘Data Winter’: Can you provide a brief overview of your concept of 'Data Winter' and why you believe we are on the brink of entering one...

Let’s talk about AI tokenization in this third episode of our AI in Action series. Tokenization is actually pretty interesting, especially if you ever wondered how these fancy AI machines understand the stuff we type and say and produce things when we give them prompts.  Next time you're marvelling at an AI-generated text, remember it's all about those tiny tokens, dancing together in a complex sy...

🔍 In this TL;DR episode, Dr. Bertin Martens (Bruegel) discusses his working paper for the Brussels-based economic think tank on the economic arguments in favour of reducing copyright protection for generative AI inputs and outputs with the AI lab * 9:44: Mr Martens intended to say "humans" instead of machines 📌 TL;DR Highlights ⏲️[00:00] Intro ⏲️[01:21] Q1-Balancing Innovation & Rights Can t...

🔍 In this TL;DR episode, Prof. Dr. Alexander Peukert (Goethe University Frankfurt am Main) discusses his primer on copyright in the EU AI Act with the AI lab 📌 TL;DR Highlights ⏲️[00:00] Intro ⏲️[01:26] Q1-Merging copyright & AI regulation: What challenges arise from merging copyright law and AI regulation? How might this impact legislation, compliance, and enforcement? ⏲️[06:08] Q2-AI Act c...

🔍 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...

Let’s talk about AI terminology in the second episode in our AI in Action series. The AI term gets thrown around more than a beach ball at a summer picnic, and it’s not always clear what people are talking about. “AI” is to tech what “food” is to a grocery store – sure, it covers a lot, but a hot dog and a filet mignon are pretty darn different when it comes to what they do to your insides. AI is...

🔍 In this TL;DR episode, Dr Elisa Giomi, Associate Professor at the Roma Tre University and Commissioner of the Italian Communications Regulatory Authority (AGCOM), discusses her recent contribution on Intermedia, the journal of the International Institute of Communications (IIC), titled “The (almost) unacknowledged revolution of AI in the media and creative industries”, with the AI lab 📌 TL;DR...

🔍 In this TL;DR episode, Derek Slater (Proteus Strategies) discusses his recent blog post on the Tech Policy Press website, titled “What the Copyright Case Against Ed Sheeran Can Teach Us About AI”, with the AI lab 📌 TL;DR Highlights ⏲️[00:00] Intro ⏲️[01:11] Q1 - Legal boundaries & creativity: How to define the boundary between protectable expression and unprotectable building blocks in mus...

We are kickstarting our AI in Action series by diving headfirst into the key milestones that led to the gradual deployment of Artificial Intelligence, or AI for short. You might think it's some shiny new invention, looking at all the recent media coverage about robots taking over your jobs and writing bad poetry. But hold on to your Roomba, because AI has been around longer than your grandma’s poc...

🔍 In this TL;DR episode, Professor Žiga Turk (University of Ljubljana, Slovenia) discusses his recent contribution for the Wilfried Martens Centre for European Studies on how “Brussels is About to Protect Citizens from Intelligence” with the AI lab 📌 TL;DR Highlights ⏲️[00:00] Intro ⏲️[01:55] Q1 - Why do you think AI regulation prioritises limiting risks over promoting innovation and freedom of...