🔍 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 behavior and applications, not “AI” as an abstract concept.

📌 TL;DR Highlights

⏲️[00:00] Intro

⏲️[00:47] Q1-Why do you see AI as “distributed computing,” not a new tech?

⏲️[04:05] Q2-Why aren’t issues like misinformation and bias new to AI?

⏲️[09:29] Q3-Why focus on regulating specific AI apps instead of AI overall?

⏲️[17:02] Wrap-up & Outro

💭 Q1 - Why do you see AI as “distributed computing,” not a new tech?

🗣️ "Distributed computing is a revolutionary technology... it started around 1945, 1950, and it's been progressing and evolving ever since."

🗣️ "What people are calling AI is really a digital ecosystem, which is a cybernetic system that consists of computing devices, networks, data, and software."

🗣️ "When people talk about governing AI, it's not just about AI... you're going to have to control everything about ICT, everything about digital ICT."

🗣️ "If indeed you're talking about some way of controlling in advance the capabilities to do machine learning applications, you really are committing yourself to controlling every element of that digital ecosystem."

💭 Q2 - Why aren’t issues like misinformation and bias new to AI?

🗣️ "People say AI is a problem because of its capability to distribute misinformation, but if you go back to 1995, we were already noticing that some of the things you find on the internet are not true."

🗣️ "In the early days of the internet, people thought that just because something was online, it had to be credible or true."

🗣️ "What was giving you these search results... was an early form of artificial intelligence. You had robots searching the internet and scanning websites according to a program."

🗣️ "What’s interesting is that AI-driven search engines were already giving biased results back in the '90s... search engine bias was something people were complaining about."

🗣️ "You are interacting with an artificial intelligence mechanism that is distributed and globalized, and you're trying to control the results or objecting to the results in ways that are very similar to what we're hearing now about AI."

💭 Q3 - Why focus on regulating specific AI apps instead of AI overall?

🗣️ "The problems posed by a specific AI application would require different kinds of responses depending on the nature of the problem."

🗣️ "Can you solve a problem like that by regulating facial recognition technology? And the answer is no. What you do is you regulate police practice."

🗣️ "You have to be more specific. It's not about regulating AI in a general way; it's about regulating specific applications in their specific context."

🗣️ "Whenever you hear somebody say 'AI is causing this problem,' just substitute the word computing, and you'll see how it makes just as much sense."

🗣️ "The idea that you can regulate technology to prevent it from doing any harm in advance is an idea that, as a historian of technology, just doesn't make any sense."

📌 About Our Guest

🎙️ Milton Mueller | the Georgia Institute of Technology School of Public Policy

🌐 Article | It's just distributed computing: Rethinking AI governance

https://www.sciencedirect.com/science/article/pii/S030859612500014X 

🌐 Milton Mueller

https://www.linkedin.com/in/miltonmueller/ 

Milton Mueller is Professor at the Georgia Institute of Technology School of Public Policy. He specialises in the political economy of information and communication and is the author of seven books and numerous journal articles. His work informs not only public policy but also science and technology studies, law, economics, communications, and international studies.

#AI #ArtificialIntelligence #GenerativeAI

🔍 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 responsible AI development and level the playing field for startups and researchers. 

📌 TL;DR Highlights

⏲️[00:00] Intro

⏲️[00:40] Q1-How do copyright laws limit AI’s training data and knowledge diffusion?

⏲️[04:05] Q2-How does the original intent of the Intellectual Property Clause conflict with current copyright rules?

⏲️[10:35] Q3-What reforms could align copyright with promoting science?

⏲️[15:36] Wrap-up & Outro

💭 Q1 - How do copyright laws limit AI’s training data and knowledge diffusion?

🗣️ "Copyright law was designed to promote knowledge creation. Now it functions as a bottleneck in that knowledge ecosystem."

🗣️ "Today's AI systems face a paradoxical constraint: they require vast oceans of human-created content to learn, and yet our copyright framework increasingly cordons off those essential waters."

🗣️ "Early publishers faced guild restrictions and royal monopolies that limited the dissemination of knowledge. Today's AI developers are navigating a similarly restrictive landscape through the barriers of copyright law."

🗣️ "When there's ambiguity in the law, that hinders innovation. It leads to litigation and poses a real threat to startups trying to determine whether they can use copyrighted information for training."

💭 Q2 - How does the original intent of the Intellectual Property Clause conflict with current copyright rules? 

🗣️ "The IP clause starts off with a mandate that Congress spread knowledge. If something isn’t promoting the progress of science, then it can’t be interpreted as constitutional."

🗣️ "Copyright began as a 14-year term. Now it's expanded to more than 70 years — a huge restriction on the ability to spread knowledge."

🗣️ "The founders hated monopolies. They’d seen how royal prerogatives were used to quash innovation — and tried to create a better system for incentivizing knowledge."

🗣️ "AI tools are unparalleled in their ability to create knowledge. The question now is: can we spread that knowledge?"

🗣️ "The Constitution’s goal wasn’t just to reward creators — it was to spread science and useful arts as far and wide as possible."

💭 Q3 - What reforms could align copyright with promoting science?

🗣️ "We need a clear statutory presumption that using works for machine learning constitutes fair use — that sort of clarity is essential for startups and research institutions to compete."

🗣️ "Without robust datasets, the positive use cases of AI — from public health breakthroughs to AI tutors for differently-abled students — simply aren’t possible."

🗣️ "Imagine if all that data your smartwatch gathers went toward training AI models tailored to the public good — that’s the promise of data commons."

🗣️ "AI is like fire: it can spread and fuel incredible progress — but if we smother it with fire extinguishers too soon, only the biggest players will be able to benefit."

🗣️ "We must make sure this isn’t a world where only OpenAI, Anthropic, and Google build the models — we need a future with many options and many positive use cases of AI."

📌 About Our Guest

🎙️ Kevin Frazier | the University of Texas at Austin School of Law

🌐 Article | Progress Interrupted: The Constitutional Crisis in Copyright Law

https://jolt.law.harvard.edu/digest/progress-interrupted-the-constitutional-crisis-in-copyright-law 

🌐 Kevin Frazier

https://www.linkedin.com/in/kevin-frazier-51811737/    

Kevin Frazier is an AI Innovation and Law Fellow at the Austin School of Law of the University of Texas. He is also a Contributing Editor at Lawfare, a non-profit publication and he developed the first open-source Law and AI syllabus.

#AI #ArtificialIntelligence #GenerativeAI

🔍 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 aims to bring legal clarity, promote interoperability, and support responsible AI development.

📌 TL;DR Highlights

⏲️[00:00] Intro

⏲️[00:41] Q1-Why is this vocabulary needed for AI training opt-outs?

⏲️[04:17] Q2-How does it help creators, AI developers, and policymakers and what are some of the concepts?

⏲️[11:55] Q3-What are its limitations, and how could it evolve?

⏲️[14:35] Wrap-up & Outro

💭 Q1 - Why is this vocabulary needed for AI training opt-outs?

🗣️ "At the core of the EU copyright framework is... the TDM exceptions – the exceptions for text and data mining that were introduced in the 2019 Copyright Directive."

🗣️ "It ensures that rights holders have some level of control over their works, and it makes sure that the majority of publicly available works are available to innovate on top of, to build new things."

🗣️ "The purpose of such a vocabulary is to provide a common language for expressing rights reservations and opt-outs that are understood in the same way along the entire value chain."

🗣️ "This vocabulary proposal is the outcome of discussions that we had with many stakeholders, including rights holders, AI companies, policymakers, academics, and public interest technologists."

💭 Q2 - How does it help creators, AI developers, and policymakers and what are some of the concepts? 

🗣️ "At the very core, the idea of vocabulary is that you have some common understanding of language... that terms you use mean the same to other people that you deal with."

🗣️ "We offer these three use cases for people to target their opt-outs from... like sort of the Russian dolls: the wide TDM category that is AI training, and in that is generative AI training."

🗣️ "If all of these technologies sort of use the same definition of what they are opting out, it becomes interoperable and it becomes also relatively simple to understand on the rights holder side."

💭 Q3 - What are its limitations, and how could it evolve?

🗣️ "The biggest limitation is... we need to see if this lands in reality and stakeholders start working with this."

🗣️ "These information intermediaries... essentially convey the information from rights holders to model providers—then it has a chance to become something that structures this field."

🗣️ "It is designed as a sort of very simple, relatively flexible approach that makes it expandable."

📌 About Our Guest

🎙️ Paul Keller | The Open Future Foundation

🌐 Article | A Vocabulary for opting out of AI training and other forms of TDM

https://openfuture.eu/wp-content/uploads/2025/03/250307_Vocabulary_for_opting_out_of_AI_training_and_other_forms_of_TDM.pdf 

🌐 Paul Keller

https://www.linkedin.com/in/paulkeller/       

Paul Keller is the co-Founder and Director of Policy at the Open Future Foundation, a European nonprofit organization. He has extensive experience as a media activist, open policy advocate and systems architect striving to improve access to knowledge and culture.

#AI #ArtificialIntelligence #GenerativeAI

🔍 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 codes of practice and contractual diligence could help align AI innovation with EU copyright rules.

#AI #ArtificialIntelligence #GenerativeAI

📌 TL;DR Highlights

⏲️[00:00] Intro

⏲️[00:51] Q1-What are the key interactions between the AI Act and EU copyright law, particularly concerning text and data mining (TDM) practices?

⏲️[7:12] Q2-How do you see the transparency obligations of the AI Act shaping the relationship between AI developers and rightsholders?

⏲️[15:22] Q3-Is the extraterritorial reference of Recital 106 of the AI Act enforceable?

⏲️[24:35] Wrap-up & Outro

💭 Q1 - What are the key interactions between the AI Act and EU copyright law, particularly concerning text and data mining (TDM) practices

🗣️ "The AI Act links to Article 4 by obliging general-purpose AI (GPAI) model providers to identify and respect rights reservation mechanisms and disclose a sufficiently detailed summary about the training data used."

🗣️ "The Copyright Directive of 2019 introduces exceptions and limitations for text and data mining (TDM), with Article 3 aimed at research and Article 4 applying broadly but with additional requirements like rights reservation or opt-out mechanisms."

🗣️ "The concept of TDM is so broad that it applies to activities involved in pre-training and training AI models, impacting entities across the value chain, not just model providers."

🗣️ "Entities like Common Crawl or LAION that perform upstream activities like web scraping are not directly regulated by the AI Act but are part of the broader TDM definition under the Copyright Directive."

🗣️ "One debated requirement is the rights reservation or opt-out mechanism for publicly accessible online content."

💭 Q2 - How do you see the transparency obligations of the AI Act shaping the relationship between AI developers and rightsholders?

🗣️ "The transparency provision in Article 53(1)(d) requires GPAI model providers to make publicly available a sufficiently detailed summary of training data, balancing interests like copyright, privacy, and fair competition."

🗣️ "If the summary is too vague, it becomes meaningless; if too detailed, it might infringe on trade secrets—so finding a balance is critical."

🗣️ "The usefulness of the training data summary lies in clarifying whether TDM exception requirements, such as lawful access and respect for opt-outs, have been met."

🗣️ "A significant challenge is ensuring compliance when data sets are obtained from upstream providers not subject to the AI Act, raising questions about responsibility and enforcement."

🗣️ "The Act balances interests, acknowledging the impossibility of listing all copyrighted works used in training due to territorial fragmentation and low originality thresholds."

💭 Q3 - Is the extraterritorial reference of Recital 106 of the AI Act enforceable?

🗣️ "Recital 106 aims to prevent regulatory arbitrage by requiring compliance with EU copyright standards, even for AI models trained outside the EU."

🗣️ "The principle of territoriality in copyright law conflicts with the extraterritorial implications of the AI Act, as copyright rules are typically governed by the location of the activity."

🗣️ "Using contractual obligations and voluntary meta-regulation, such as commitments from upstream providers, offers a more consistent way to enforce compliance than extending the law extraterritorially."

🗣️ "The Act's compliance incentives might still push GPAI providers to align with EU standards to avoid severe sanctions, even if extraterritorial enforcement remains uncertain."

🗣️ "Some suggest contractual obligations or meta-regulation as more practical solutions to ensure upstream compliance with EU law."

📌 About Our Guest

🎙️  João Pedro Quintais | Assistant Professor, Institute for Information Law (IViR)

🌐 Article | Generative AI, Copyright, and the AI Act

https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4912701 

🌐 Joao Pedro Quintais

https://www.ivir.nl/employee/quintais/ 

Dr João Pedro Quintais is Assistant Professor at the University of Amsterdam’s Law School, in the Institute for Information Law (IViR). João notably studies how intellectual property law applies to new technologies and the implications of copyright law and its enforcement by algorithms on the rights and freedoms of Internet users, on the remuneration of creators, and on technological development. João is also Co-Managing Editor of the widely read Kluwer Copyright Blog and has published extensively in the area of information law.

🔍 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 need for nuanced preference signals to preserve openness while respecting creators' intentions in the age of generative AI.

📌 TL;DR Highlights

⏲️[00:00] Intro

⏲️[00:38] Q1-How has the conversation around creator consent and AI evolved in the past few years?

⏲️[05:42] Q2-How has the tension between openness and creator choice played out so far, and what lessons can be learned from this tension?

⏲️[11:02] Q3-can we ensure that marginalized or underrepresented communities maintain agency over their contributions to the commons?

⏲️[19:03] Wrap-up & Outro

💭 Q1 - How has the conversation around creator consent and AI evolved in the past few years?

🗣️ "When AI became mainstream, most creators couldn’t have anticipated how their works might one day be used by machines—some uses align with their intentions, but others do not."

🗣️ "Individuals want more choice over how their work is used, and without tailored options, some are considering paywalls or not publishing at all."

🗣️ "The EU AI Act’s opt-out mechanism is a blunt instrument—it’s just a 'no,' not a nuanced reflection of creators’ varied preferences."

🗣️ "Creators may object to large companies using their works for AI training but be fine with nonprofits or research-focused uses, showing the need for more nuanced tools."

🗣️ "We’re focusing on developing 'preference signals'—mechanisms that let creators communicate specific preferences for how their work is used in AI models."

💭 Q2 - How has the tension between openness and creator choice played out so far, and what lessons can be learned from this tension?

🗣️ "Scientists and researchers who traditionally embraced open access are now reconsidering, fearing that commercial AI providers are exploiting their work."

🗣️ "Creators who once freely shared their work under CC licenses are now hesitant, either because they misunderstand AI training risks or feel exposed."

🗣️ "The worst outcome of this tension is less openness overall—creators retreating behind paywalls or choosing not to publish at all."

🗣️ "A perception persists in the open-source AI community that CC-licensed works are 'safe' to use, but creators’ motivations for sharing openly years ago don’t always align with today’s AI landscape."

🗣️ "To preserve openness while respecting creator intentions, we need mechanisms that enable a 'no unless' approach—minimizing restrictions while maximizing use."

💭 Q3 - can we ensure that marginalized or underrepresented communities maintain agency over their contributions to the commons?

🗣️ "Generative AI amplifies existing inequalities because it demands infrastructure like electricity, internet, and computing power—resources many regions lack."

🗣️ "Even if everyone had equal internet access, a one-size-fits-all approach to technology wouldn’t work due to local contexts and different needs."

🗣️ "Traditional knowledge should be exempt from broad data mining rights, allowing communities to explicitly give or revoke permissions for its use in AI training."

🗣️ "We need public AI infrastructures that ensure diversity and regional perspectives while maintaining communities’ agency over their contributions."

🗣️ "To prevent lopsided development, policies must go beyond tools like preference signals and address broader governance and societal frameworks."

📌 About Our Guest

🎙️ Anna Tumadóttir | Creative Commons

🌐 Article | Questions for Consideration on AI & the Commons

https://creativecommons.org/2024/07/24/preferencesignals/ 

🌐 Anna Tumadóttir 

https://creativecommons.org/person/annacreativecommons-org/

Anna is the CEO of Creative Commons, an international nonprofit organization that empowers people to grow and sustain the thriving commons of shared knowledge and culture.

#AI #ArtificialIntelligence #GenerativeAI

🔍 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 trap.

📌 TL;DR Highlights

⏲️[00:00] Intro

⏲️[00:46] Q1-What is the "Copyright Trap," and why could it harm AI and creativity?

⏲️[10:05] Q2-Can you explain the three routes that lead into the copyright trap and their relevance to AI?

⏲️[22:08] Q3-What alternatives should policymakers consider to protect creators and manage AI?

⏲️[28:45] Wrap-up & Outro

💭 Q1 - What is the "Copyright Trap," and why could it harm AI and creativity?

🗣️ “To turn to copyright law is to turn to really a false friend. The idea that copyright is going to be our friend, is going to help us in this situation,(...) it's likely to do more harm than good."

🗣️ “We are imagining increasingly in these policy debates that copyright and protection of copyright owners will be a kind of counterweight to corporate power and to the sort of extractive logics of Big Tech and AI development. I think that that is misguided. And in fact, we're playing into the interests of both the entertainment industries and big tech ”

🗣️ "When we run into the copyright trap, this sort of conviction that copyright is going to be the right regulatory tool, we are sort of defining how this technology is going to evolve in a way that I think will backfire and will actually undermine the political objectives of those who are pointing to the inequities and the unfairness behind the technology and the way that it's being developed.”

🗣️ "AI industry, big tech industry and the creative industry stakeholders are all, I think, perfectly happy to approach these larger policy questions through the sort of logic of copyright, sort of proprietary logic of ownership, control, exchange in the free market, licencing structures that we're already seeing taking hold"

🗣️ "What we're going to see, I think, if we run into the copyright trap is that certainly smaller developers, but really everyone will be training the technology on incomplete data sets, the data sets that reflect the sort of big packaged data products that have been exchanged for value between the main market actors. So that's going to lessen the quality really of what's going in generally by making it more exclusive and less inclusive."

💭 Q2 - Can you explain the three routes that lead into the copyright trap and their relevance to AI?

🗣️ ""The first route that I identify is what's sometimes called the if-value-then-right fallacy. So that's the assumption that if something has value, then there should be or must be some right over it.“

🗣️ "Because something has value, whether economic or social, doesn't mean we should turn it into property that can be owned and controlled through these exclusive rights that we find in copyright law." 

🗣️ "The second route that I identify is a sort of obsession with copying and the idea that copying is inherently just a wrongful activity. (...) The reality is that there's nothing inherently wrongful about copying. And in fact, this is how we learn. This is how we create.

🗣️ "One of the clearest routes into the copyright trap is saying, well, you know, you have to make copies of texts in order to train AI. So of course, copyright is implicated. And of course, we have to prevent that from happening without permission.. (...) But our obsession with the individuated sort of discrete copies of works behind the scenes is now an anachronism that we really need to let go.”

🗣️ "Using the figure of the artist as a reason to expand copyright control, and assuming that that's going to magically turn into lining the pockets of artists and creators seems to me to be a fallacy and a route into the copyright trap."

💭 Q3 - Why is output-based remuneration better for creators, AI developers, and society?

🗣️ "The health of our cultural environment (..) [should be] the biggest concern and not simply or only protecting creators as a separate class of sort of professional actors."

🗣️ "I think what we could do is shift our copyright policy focus to protecting and encouraging human authorship by refusing to protect AI generated outputs.

🗣️ "If the outputs of generative AI are substantially similar to works on which the AI was trained, then those are infringing outputs and copyright law will apply to them such that to distribute those infringing copies would produce liability under the system as it currently exists.“

🗣️ "There are privacy experts who might be much better placed to say how should we curate or ensure that we regulate the data on which the machines are trained and I would be supportive of those kinds of interventions at the input stage.

🗣️ “Copyright seems like a tempting way to do it but that's not what it does. And so maybe rather than some of the big collective licencing solutions that are being imagined in this context, we'd be better off thinking about tax solutions, where we properly tax big tech and then we use that tax in a way that actually supports the things that we as a society care about, including funding culture and the arts."

📌 About Our Guest

🎙️ Carys J Craig | Osgoode Hall Law School

🌐 Article | The AI-Copyright Trap

https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4905118 

🌐 Carys J Craig

https://www.osgoode.yorku.ca/faculty-and-staff/craig-carys-j/  

Carys is the Academic Director of the Osgoode Professional Development LLM Program in Intellectual Property Law, and recently served as Osgoode’s Associate Dean. A recipient of multiple teaching awards, Carys researches and publishes widely on intellectual property law and policy, with an emphasis on authorship, users’ rights and the public domain.

#AI #ArtificialIntelligence #GenerativeAI

🔍 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 Highlights

⏲️[00:00] Intro

⏲️[00:53] Q1-How did the 2019 Copyright Directive change the landscape for cultural heritage institutions in terms of text and data mining?

⏲️[05:07] Q2-Why are some cultural heritage institutions choosing to opt-out of text and data mining and what are the challenges involved?

⏲️[11:27] Q3-What are the broader implications of these opt-outs for research, smaller institutions, and open access to cultural content?

⏲️[14:53] Wrap-up & Outro

💭 Q1 - How did the 2019 Copyright Directive change the landscape for cultural heritage institutions in terms of text and data mining?

🗣️ "The 2019 Directive was expected to open up possibilities for cultural heritage institutions to continue their public interest mission with the help of technology."

🗣️ "The first big use of text and data mining techniques is to facilitate cultural heritage institutions’ day-to-day work.It’s rare to see cultural heritage institutions preparing datasets for public text and data mining activities."

🗣️ "More institutions are leaning toward putting barriers on data use rather than encouraging it.Instead of embracing possibilities, there’s unnecessary caution in the cultural heritage sector around AI."

💭 Q2 - Why are some cultural heritage institutions choosing to opt-out of text and data mining and what are the challenges involved?

🗣️ "The only fully legitimate reason for opting out is when the rights holder explicitly requests it."

🗣️ "Cultural heritage institutions rarely own the copyright for the materials they hold, making enforcement of opt-outs challenging."

🗣️ "Confusion about the legal framework leads some institutions to fear they must protect data from misuse."

🗣️ "By opting out, institutions risk missing out on positive uses of their data due to fear of negative outcomes."

🗣️ "Cultural heritage institutions have a public interest mission to safeguard access and encourage the use of their information."

💭 Q3 - What are the broader implications of these opt-outs for research, smaller institutions, and open access to cultural content?

🗣️ "Some organisations block access to avoid supporting big players in activities perceived as unethical."

🗣️ "Opting out doesn’t weaken monopolistic practices but harms smaller players who can’t access the data."

🗣️ "Institutions must balance the implications of their decisions on access with the potential for positive uses."

🗣️ "Aggressive crawling that disrupts public services may justify access restrictions in certain cases."

🗣️ "Overly broad decisions could limit the positive applications of text and data mining techniques on cultural heritage data."

📌 About Our Guest

🎙️ Ariadna Matas | Europeana Foundation

🌐 Article | AI ‘opt-outs’: should cultural heritage institutions (dis)allow the mining of cultural heritage data?

AI ‘opt-outs’: should cultural heritage institutions (dis)allow the mining of cultural heritage data?

🌐 Ariadna Matas

https://pro.europeana.eu/person/ariadna-matas

Ariadna is Policy Advisor at the Europeana Foundation, an independent, non-profit organisation that stewards the common European data space for cultural heritage and contributes to other digital initiatives that put cultural heritage to good use in the world. 

#AI #ArtificialIntelligence #GenerativeAI

🔍 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 truly benefit individual creators. A proposal is made to focus regulatory efforts on the market exploitation phase of AI systems, ensuring compensation flows back to creative industries and authors through well-managed redistribution mechanisms.

📌 TL;DR Highlights

⏲️[00:00] Intro

⏲️[01:04] Q1-How does the EU's current approach to AI training and copyright hinder innovation and fair pay for authors?

⏲️[04:53] Q2-What’s your alternative to balance author compensation and AI development?

⏲️[06:50] Q3-Why is output-based remuneration better for creators, AI developers, and society?

⏲️[09:23] Wrap-up & Outro

💭 Q1 - How does the EU's current approach to AI training and copyright hinder innovation and fair pay for authors?

🗣️ "What policymakers try to do in this space where we try to reconcile AI innovation with traditional copyright goals is: first of all, of course we want the best AI systems and we want the least biassed AI systems."

🗣️ "The regulation puts a heavy, heavy burden on AI training by requiring to take into account rights reservations, the so-called opts-outs."

🗣️ "You might not get the best AI systems if you put all these burdens on the AI training process."

🗣️ "The moment you allow rights holders to opt out and to remove certain resources from training, then of course you no longer know whether you get the least biassed AI systems."

🗣️ "The simple fact that a big creative industry right holder receives some extra money doesn't mean that this money is passed on to the individual authors really doing the creative work."

💭 Q2 - What’s your alternative to balance author compensation and AI development?

🗣️ "If we imagine a regulation that leaves this development phase totally unencumbered by copyright burdens, you give lots of freedom for AI developers to use all the resources they think are necessary."

🗣️ "Once we have these fully developed, high potential AI systems and these systems are brought to the market, (...) You place a tax, a burden on the AI systems, not at the development stage, but at the moment where they are exploited in the marketplace.”

🗣️ "The money finally flows back in the form of compensation to the creative industry and individual authors."

💭 Q3 - Why is output-based remuneration better for creators, AI developers, and society?

🗣️ "From a European perspective, it's quite easy to propose that this should be collecting societies because we have a very well developed system of collective rights management in the area of copyright."

🗣️ "In the case of AI output, you can also use data from the systems itself: to which extent is a certain style, a certain genre prominent in prompts that users enter? What type of AI output is generated and to which extent does it resemble certain pre-existing human works and creations? What is the market share on the more general market for literary, artistic expression and so on?"

🗣️ "Traditionally, repartitioning schemes have a split between money that is directly given to individual authors and money that is given to the industry.We have a guarantee that a certain percentage of the money will directly reach the individual authors and performers, and will not stay at industry level exclusively."

📌 About Our Guest

🎙️ Martin Senftleben | Institute for Information Law (IViR) and University of Amsterdam

🌐 Article | Win-win: How to Remove Copyright Obstacles to AI Training While Ensuring Author Remuneration (and Why the European AI Act Fails to Do the Magic)

Win-win: How to Remove Copyright Obstacles to AI Training While Ensuring Author Remuneration (and Why the European AI Act Fails to Do the Magic)

🌐 Martin Senftleben

linkedin.com/in/martin-senftleben-2430aa5b           

Martin Senftleben is Professor of Intellectual Property Law and Director, Institute for Information Law (IViR), University of Amsterdam. His activities focus on the reconciliation of private intellectual property rights with competing public interests of a social, cultural or economic nature. He publishes extensively on these topics and lectures across the globe.

#AI #ArtificialIntelligence #GenerativeAI

🔍 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 an evolution of copyright?
⏲️[03:58] Q2-Can we expect new forms of legal recognition or protection for prompts?
⏲️[06:13] Q3-Are current copyright rules able to address authorship in genAI works or do we need new legal categories?
⏲️[08:00] Wrap-up & Outro

💭 Q1 - How does genAI challenge traditional copyright doctrines and will this lead to an evolution of copyright?

🗣️ "Copyright law has always tried to protect creative expression but is careful not to protect the idea behind a work."
🗣️ "Generative AI changes the normal economics and dynamics of creation by doing the hard work for us, like making the painting or doing the actual brushstrokes."
🗣️ "If copyright law doesn’t protect the expression created by AI rather than by a person, the question is, what, if anything, is there to copyright?"
🗣️ "Generative AI blows up the substantial similarity test because it’s unclear whether two similar works came from the same prompt or if the AI just made the same thing."
🗣️ "I might copy your prompt, input it into generative AI, and get a different output—making similarity no longer the evident marker of copying."

💭 Q2 - Can we expect new forms of legal recognition or protection for prompts?

🗣️ "We're still litigating whether the material generated by AI can be copyrighted, but we may ultimately say yes, as with photography 150 years ago."
🗣️ "Courts may get comfortable with the idea that structuring the prompt and iterating it is a form of creativity that leads to the final output."
🗣️ "In early photography, we gave copyright protection even though the machine made the image, because human judgment helped determine the outcome."
🗣️ "Prompt engineering could become more sophisticated, leading courts to see creativity in how prompts are structured and refined."
🗣️ "Sometimes I just ask a very simple question, and if that's all I contribute, I’m not sure there’s any protection."

💭 Q3 - Are current copyright rules able to address authorship in genAI works or do we need new legal categories?

🗣️ "There may be something around the creativity of prompts that will matter, but we're not there yet in terms of case law."
🗣️ "The assumption that 'I made a movie, I wrote text, so I get copyright in that work' is going to be called into question in the generative AI context."
🗣️ "Movie studios or video game companies that use AI to save money might be shocked when other people are free to copy AI-generated backgrounds."
🗣️ "Even if we get copyright protection for AI outputs, it will occupy a weird middle ground that feels different from what we’re used to."
🗣️ "There’s going to be pressure to change the law to make it align more with what copyright industries have been comfortable with, but it won’t be easy."

📌 About Our Guest
🎙️ Mark Lemley | Stanford Law School
🌐 Article | How Generative AI Turns Copyright Upside Down
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4517702 
🌐 Mark Lemley
https://law.stanford.edu/mark-a-lemley/

Mark is William H. Neukom Professor of Law at Stanford Law School and the Director of the Stanford Program in Law, Science and Technology. He teaches intellectual property, patent law, trademark law, antitrust, the law of robotics and AI, video game law, and remedies and he is the author of 11 books and 218 articles.

🔍 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 content conflict with the principles of free speech and access to information?

⏲️[06:53] Q2-Could AI chatbot self-censorship conflict with the systemic risk provisions of the Digital Services Act (DSA)?

⏲️[10:20] Q3-What changes would you recommend to better align chatbot moderation policies with free speech protections?

⏲️[15:18] Wrap-up & Outro

💭 Q1 - How does the high rate of refusal by chatbots to generate content conflict with the principles of free speech and access to information?

🗣️ "This is the first time in human history that new communications technology does not solely depend on human input, like the printing press or radio."

🗣️ "Limiting or restricting the output and even the ability to make prompts will necessarily affect the underlying capability to reinforce free speech, and especially access to information."

🗣️ "If I interact with an AI chatbot, it's me and the AI system, so it seems counterintuitive that the restrictions on AI chatbots are more wide-ranging than those on social media."

🗣️ "Would it be acceptable to ordinary users to say, you're writing a document on blasphemy, and then Word says, 'I can't complete that sentence because it violates our policies'?"

🗣️ "The boundary between freedom of speech being in danger and freedom of thought being affected is a very narrow one."

🗣️ "Under international human rights law, freedom of thought is absolute, but algorithmic restrictions risk subtly interfering with that freedom.(...) These restrictions risk being tentacles into freedom of thought, subtly guiding us in ways we might not even notice."

💭 Q2 - Could AI chatbot self-censorship conflict with the systemic risk provisions of the Digital Services Act (DSA)?

🗣️ "The AI act includes an obligation to assess and mitigate systemic risk, which could be relevant here regarding generative AI’s impact on free expression."

🗣️ "The AI act defines systemic risk as a risk that is specific to the high-impact capabilities of general-purpose AI models that could affect public health, safety, or fundamental rights."

🗣️ "The question is whether the interpretation under the AI act would lean more in a speech protective or a speech restrictive manner."

🗣️  "Overly broad restrictions could undermine freedom of expression in the Charter of Fundamental Rights, which is part of EU law."

🗣️ "My instinct is that the AI act would likely lean in a more speech-restrictive way, but it's too early to say for certain."

💭 Q3 - What changes would you recommend to better align chatbot moderation policies with free speech protections?

🗣️ "Let’s use international human rights law as a benchmark—something most major social media platforms commit to on paper but don’t live up to in practice."

🗣️ "We showed that major social media platforms' hate speech policies have undergone extensive scope creep over the past decade, which does not align with international human rights standards."

🗣️ "It's conceptually more difficult to apply international human rights standards to an AI chatbot because my interaction is private, unlike public speech."

🗣️ "We should avoid adopting a 'harm-oriented' principle to AI chatbots, especially when dealing with disinformation and misinformation, which is often protected under freedom of expression."

🗣️ "It's important to maintain an iterative process with AI systems, where humans remain responsible for how we use and share information, rather than placing all the responsibility on the chatbot."

📌 About Our Guest

🎙️ Jacob Mchangama | The Future of Free Speech & Vanderbilt University

 𝕏  https://x.com/@JMchangama

🌐 Article | AI chatbots refuse to produce ‘controversial’ output − why that’s a free speech problem

https://theconversation.com/ai-chatbots-refuse-to-produce-controversial-output-why-thats-a-free-speech-problem-226596

🌐 The Future of Free Speech

https://futurefreespeech.org 

🌐 Jacob Mchangama

http://jacobmchangama.com 

Jacob Mchangama is the Executive Director of The Future of Free Speech and a Research Professor at Vanderbilt University. He is also a Senior Fellow at The Foundation for Individual Rights and Expression (FIRE) and author of “Free Speech: A History From Socrates to Social Media”.