AI lab by information labs

🔍 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 Senftlebenlinkedin.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, 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 problemhttps://theconversation.com/ai-chatbots-refuse-to-produce-controversial-output-why-thats-a-free-speech-problem-226596🌐 The Future of Free Speechhttps://futurefreespeech.org 🌐 Jacob Mchangamahttp://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”.

🔍 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?⏲️[05:05] Q2-Generative AI-nxiety:What are some of the most significant challenges currently hindering public access to social media and climate data, and the effects of Generative AI-nxiety?⏲️[07:49] Q3-‘Decade for Data’:Could you outline what the “Decade for Data” initiative entails and how it could transform data stewardship and collaboration?⏲️[12:25] Wrap-up & Outro💭 Q1-‘Data Winter’🗣️ At the time of an AI summer, when everyone suddenly is excited about the potential ofgenerative AI (...) for public interest purposes, (...) we are actually entering a data winter.🗣️ What I’ve witnessed the last few months, and that’s mainly as a result of advances in artificial intelligence, is that we actually see a backtracking of the progress that we’ve made in society as it relates to opening up data for public interest purposes.🗣️ Social media platforms such as X, but also Facebook, have closed down access to some of their data for research and for data journalism purposes as well.🗣️ Science data, such as climate science data, which was typically open science, has now become commercialised and is becoming proprietary data enclosed for many in society.🗣️ The initial data that was available for training data has now also become much harder to access, a result of concerns that some of that data has been extracted without a return to the data holder.💭 Q2-Generative AI-nxiety 🗣️ Some of the data that typically was available through APIs has now been closed off, and so some are calling this the post-API environment that we're currently in, where data was easily available through an API now is actually much harder to access unless one pays for it.🗣️ New licensing is being used to actually shield off the data for public interest purposes as well. So there are a whole range of vehicles that exist to enclose data that actually makes it much harder to access it for reuse.🗣️ We see a decline in access to Wikipedia, a decline in people accessing Wikipedia, and a decline in people contributing to Wikipedia, mainly because they fear that whatever they contribute will be used as training fodder for generative AI purposes.🗣️ Initiatives like Wikipedia, which are to a large extent the main source of a lot of the training data of generative AI services, are currently also suffering from AI extraction because they are dependent on voluntary contributions by the audience and the participants.🗣️ As a result, we are entering a data winter, which if we are not careful (...) may actually affect the AI summer that we currently have as well.💭 Q3-‘Decade for Data’ 🗣️ I’ve been calling for, together with others, such as the United Nations University, a Decade for Data, which is a typical way the United Nations often operates, to feature a problem and then have a well-defined strategy to address that problem.🗣️ A Decade for Data would have multiple components, one being advancing data collaboration, where you actually have new models of data being shared, including data commons, which can be updated in the current AI environment.🗣️ We need a new reimagined profession of data stewards that are individuals or teams who have the sophistication and competencies to provide access to data in a systematic, sustainable, and responsible manner.🗣️ A Decade for Data would also involve rethinking data governance and embedding digital self-determination in data governance to go beyond the current paradox of consent, facilitating access in a way that aligns with perceptions, expectations, and preferences of communities.🗣️ Establishing a social license for reuse is key, where you understand the preferences and expectations of communities and individuals, translating that into a social license so that data can be reused in a way that is trusted and aligned with community expectations.📌 About Our Guest🎙️ Dr. Stefaan G. Verhulst | Co-Founder, The GovLab & The Data Tank🌐 Frontiers Policy Labs | Are We Entering a Data Winter?https://policylabs.frontiersin.org/content/commentary-are-we-entering-a-data-winter 🌐 The Data Tankhttps://datatank.org 🌐 GovLabhttps://thegovlab.org 🌐 Dr. Stefaan G. Verhulsthttps://www.linkedin.com/in/stefaan-verhulst Dr. Stefaan G. Verhulst co-founded several research organisations, including the GovLab (New York) and The DataTank (Brussels). He focuses on using advances in science and technology, including data and AI, to improve decision-making and problem-solving and has been recognized as one of the 10 Most Influential Academics in Digital Government globally.

🔍 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 & RightsCan the TDM opt-out right hinder innovation and economic growth, and what does it mean as regards the power of copyright holders vs. the potential societal benefits of generative AI?⏲️[05:42] Q2-Licensing Impact on EU AI CompetitivenessWhat are the implications of licensing for genAI as regards competitiveness and quality of models and potential economic disadvantages for EU AI developers?⏲️[09:11] Q3-GenAI's Impact on Creative Industries & EconomyLooking at outputs, how could genAI impact the creative industries and the broader economy, and what are your thoughts on how policy should evolve to reflect this?⏲️[13:08] Wrap-up & Outro💭 Q1-Balancing Innovation & Rights🗣️ Copyright is an economic policy tool to stimulate investment in the production of artwork, and granting an exclusive copyright to an author avoids free writing on that artwork that would undermine the incentives to invest in its production.🗣️ The optimal scope of copyright protection should balance, on the one hand, the welfare losses from this exclusive right given to an author against the welfare gains for society from stimulating investment in new and innovative productions.🗣️ Both [copyright] overprotection and underprotection are bad. They will hamper innovation and reduce the economic efficiency of copyright.🗣️ Generative AI opens up new and much cheaper possibilities to produce new and innovative artwork, and also has applications in a wide variety of other sectors outside the media sector and across the economy.🗣️ The AI Act and the copyright law in Europe give priority to the private interest of copyright holders over the wider interest of society, and I don't think that's a good thing and we should change that.💭 Q2-Licensing Impact on EU AI Competitiveness🗣️ Generative AI models require vast amounts of training data to develop the model and to have a high-quality model. And already today we observe that the largest and most advanced models are running out of high-quality human edited text for model training.🗣️ There is still sufficient supply of low-quality text data, for instance from social media, or from the transposition of voice data into text, or even from synthetic data. But all these low-quality sources reduce the quality of generative AI models.🗣️ Imposing copyright licensing requirements on text data for model training will further shrink the available supply of text data for model training, and that will further reduce the quality of these models.🗣️ Only the biggest tech companies can actually afford to negotiate the licensing fees and pay those fees to copyright holders, while smaller AI startups cannot afford this and are pushed out of the market.🗣️ Pushing smaller AI startups out of the market is bad for competition, bad for innovation in the AI setting, and this is not the way we want to go.💭 Q3-GenAI's Impact on Creative Industries & Economy🗣️ Generally, copyright law worldwide grants copyright only to human authors of artwork, not to machine-produced artwork. With the arrival of generative AI models, however, that has changed, and for the first time in human history, a machine can produce artwork output.🗣️ From an economic perspective, there is no need to grant copyright to AI-produced artwork because the marginal cost of producing generative AI output is actually very close to zero (...) and the risk of free riding, therefore, is very limited.🗣️ The human labor that goes into designing a prompt set that you feed into a generative AI model is costly, and this prompt set is human artwork and could indeed receive copyright protection, just like any other human design, text or computer code.📌 About Our Guest🎙️ Dr. Bertin Martens | Senior fellow at Bruegel and non-resident research fellow at TILEC, Tilburg University🌐 Bruegel | Economic Arguments in Favour of Reducing Copyright Protection for Generative AI Inputs and Outputshttps://www.bruegel.org/working-paper/economic-arguments-favour-reducing-copyright-protection-generative-ai-inputs-and🌐 Bruegelhttps://www.bruegel.org🌐 Tilburg Law & Economics Centre (TILEC)https://www.tilburguniversity.edu/research/institutes-and-research-groups/tilec🌐 Dr. Bertin Martens https://www.bruegel.org/people/bertin-martensDr. Bertin Martens is a Senior fellow at Bruegel and a non-resident research fellow at the Tilburg Law & Economics Centre (TILEC, Tilburg University). He has worked on digital economy issues as a senior economist at the European Commission's Joint Research Centre for over a decade until April 2022. Before that, he was deputy chief economist for trade policy at the EC.

🔍 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 Modelshttps://doi.org/10.1007/s40319-023-01419-3 🌐 Prof. Thomas Margonihttps://www.law.kuleuven.be/citip/en/staff-members/staff/00137042Dr 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).

🔍 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 Highlights⏲️[00:00] Intro⏲️[01:15] Q1 - AI’s impact vs. past revolutions:How does AI’s impact on media and creative industries compare to historical technological revolutions? ⏲️[05:22] Q2 - Navigating AI in media:How should we balance AI’s benefits in combating misinformation vs its potential risks? ⏲️[11:18] Q3 - Balancing copyright & AI:You state that: “[AI] and human intelligence follow [a] not dissimilar logic. So we should not use a double standard to regulate them”. What should a balanced approach to copyright in AI look like?⏲️[17:48] Wrap-up & Outro💭 Q1 - AI’s impact vs. past revolutions🗣️ The [AI] revolution (...) in the media and creative industries, as many previous ones, will probably be declared a revolution only long after it happened.🗣️ AI in the media sector[:] Its disruptive effect goes unnoticed (...), [and] the media and creative industries remain under the radar in the public debate, since they are not among the leading adoption fields.🗣️ Two of the winners of the last Pulitzer Prize for journalism admitted using AI systems in their investigation and getting so many benefits from AI.🗣️ Why the AI revolution looks like the main technological revolutions of the past? Its ability to divide [and] polarise, the public debate between enthusiasts (...) and radical opponents (...).💭 Q2 - Navigating AI in media🗣️ Every technological innovation has been accompanied by a sort of squinting effect which leads to amplifying the distorted uses to the detriment of the more abundant beneficial applications.🗣️ Demonising AI for fear of its side effects would be as if in the past we had refused to switch from the plough to the tractor for fear that the tractor could pollute or run over people and animals.🗣️ AI is not only used to produce fake news and misleading content, but also in fact checking and identifying deepfakes. It is used in fighting disinformation.🗣️ Only by taking into account opportunities and risks at the same time, we will be able to develop a balanced regulation and avoid emergency and radical responses in the wake of moral panics produced by AI misuses.🗣️ The media (...) are likely to shape our perception of the world and to guide other choices, so they should have been included in the [EU AI Act] high-risk sectors.💭 Q3 - Balancing copyright & AI🗣️ I have strong misgivings about the remuneration hypothesis[:] (...) it privileges publishers over any other content producers.🗣️ I’m not sure having different rules for the human and artificial mind makes sense. My conclusion here is that maybe it’s too early to find a solution to the copyright problems raised by AI.🗣️ Any balanced resolution must have two starting points: first, a rigorous analysis of the real value chain (...), and second, (...) [a] precise diagnosis. (...) Regulate only when there is a [real] pathology to be healed.📌 About Our Guest🎙️ Dr Elisa Giomi | Associate Professor at Roma Tre University & Commissioner of the Italian Communications Regulatory Authority (AGCOM) 𝕏 https://x.com/@elisagiomi🌐 International Institute of Communications (IIC) - The (Almost) Unacknowledged Revolution of AI in the Media and Creative Industrieshttps://iicintermedia.org/vol-52-issue-1/the-almost-unacknowledged-revolution-of-ai-in-the-media-and-creative-industries/🌐 AGCOM - Dr Elisa Giomihttps://www.agcom.it/elisa-giomiDr Elisa Giomi is an associate professor at Roma Tre University, Department of Philosophy, Communication and Performing Arts, and a commissioner of AGCOM, the Italian Communications Regulatory Authority. Professor Giomi is the author of a wide array of publications for major Italian and international publishers and peer-reviewed journals.#AI #ArtificialIntelligence #GenerativeAI

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 pocket calculator.Read more & grab the infographic of this timeline here:https://informationlabs.org/ai-lab-ai-in-action-episode-01-ai-history/