How to Protect Artists' Rights in AI

If you're a photographer or creative and you haven't updated your licensing agreements for AI, you need to do it now. Here's what's happening: brands are taking the work you license to them and feeding it into AI tools to generate more content. They're using your images to train models, create variations, or build entirely new assets without paying you a dime extra. And if your contract doesn't explicitly prohibit this, they're probably not breaking any rules. So here's what needs to be in your licensing agreements moving forward: 🚫 Explicitly prohibit AI usage. Your contract should state that the licensed work cannot be used to train AI models, generate derivative works through AI, or be input into any generative AI tools. Period. ✅ Define what counts as AI use. Be specific. This includes but isn't limited to: training machine learning models, creating AI-generated variations, using the work as reference material for AI tools, or any computational analysis that results in new content. 🤑 Charge separately if they want AI rights. If a client wants to use your work for AI purposes, that's a separate license with a separate (much higher) fee. This isn't standard usage, it's essentially giving them the ability to replicate your style infinitely. 🧐 Include audit rights. Give yourself the right to request proof of how your work is being used. If you suspect they're violating the terms, you should be able to verify. 💣 Specify penalties for violations. What happens if they break these terms? Spell it out. Additional fees, legal action, whatever makes sense. I'm not a lawyer, so please get actual legal advice when drafting contracts. But if you're not thinking about AI in your licensing terms, you're not only leaving money on the table but you're losing control of your work. The industry is changing fast. Your contracts need to keep up. 🤍

🎨 Have you ever considered the lengths to which artists must go to protect their creations in the age of AI? The University of Chicago's latest project, Nightshade, offers a fascinating solution. This tool "poisons" image data to safeguard artists' works from being used to train AI models without consent, a growing concern in the digital art community. Nightshade operates by subtly altering the pixels in images, tricking AI models into misinterpreting the content. This clever manipulation can render the data useless for training purposes, giving artists a new weapon in their arsenal against unauthorized use of their work. The project, led by computer science professor Ben Zhao, is not aimed at destroying AI companies but rather at forcing them to recognize and compensate artists' rights. Here are three key takeaways about Nightshare: - Nightshade can corrupt AI models with fewer than 100 "poisoned" samples, leading them to generate completely unrelated images to the original prompts. - The tool is part of a broader effort, including Glaze, another project by Zhao's team, which distorts AI models' perception of artistic style to prevent mimicry. - Despite criticism and legal concerns, Nightshade is seen as a legal and ethical form of defense for artists against the predatory practices of some AI companies. How will the ongoing battle between protecting artists' rights and advancing AI technology evolve, and what other creative solutions might emerge in this dynamic landscape? Learn more about Nightshade here: https://lnkd.in/d_uMwEEy

🚨 Fascinating AI paper alert: "Consent and Compensation: Resolving Generative AI’s Copyright Crisis" by Frank Pasquale & Haochen Sun is a must-read for everyone interested in AI, copyright, and artists' rights. Quotes: "The opacity and scale of AI systems is disrupting the knowledge ecosystem by significantly eroding authors’ proprietary control of their works, well beyond extant digital practices that have already undermined many authors’ well-being. Whereas prior scraping at scale tended to be focused on the non-expressive aspects of works (such as facts), AI is focused by many prompts on their expressive dimensions. Search engines have historically provided links which lead users to works themselves. In contrast, AI tends to provide substitutes for such works, while failing to provide citations to the works in the dataset most similar to the texts, images, and videos it presents as a computed synthesis." (pages 8-9) - "Under the proposed mechanism, copyright owners can first request AI providers to take actions to effectively prevent their systems from generating outputs that appear identical or substantially similar to relevant copyrighted works. A copyright owner would be entitled to send a notice to an AI provider when he or she identifies that an output generated by the provider’s AI system contains either a verbatim or substantially similar copy of his or her work, or a derivative work. In the notice, the copyright owner would be obliged to document the unauthorized reproduction of the work and his or her copyright ownership, along with a digital copy or an online link to the work." (page 21) - "Given the complexity of the AI supply chain, particularly with respect to generative AI, it is not feasible to impose a per-device cost on AI providers. However, other triggers for payment are possible. Levies on the use of particular datasets may be imposed, or on model training, or on some aggregate number of responses provided to users, or on paid subscriptions. Alternatively, the level of the levy could be benchmarked with respect to some percentage of AI providers’ expenditures or revenues" (page 39) ➡ Link to the paper below. #AI #copyright #consent #AIregulation #AIpolicy #AItraining

The music industry is finally having a real conversation about AI, and it's encouraging to see more players joining it. At Deezer, we were among the first to confront the challenges of generative AI head-on. With that experience, I want to clarify a few things. AI is not the real threat to music. Used creatively, AI can generate value and open the door for more people to make art, something fundamental to human well-being. But AI-generated music also has negative effects the industry must tackle, faster than we currently are. ISSUE #1 : IP and royalty theft The numbers speak for themselves: → More than 50% of songs uploaded to Deezer daily are now 100% AI-generated, created with models facing lawsuits from most rightsholders. → Up to 85% of streams on those songs are fraudulent, attempting to game royalty distribution. What we've done: Deezer built a detection tool to identify fully AI-generated tracks. Combined with our fraud detection systems, it lets us exclude them from royalty distribution, protecting the revenue pool for real artists. What needs to happen next: → We welcome the deals between labels and AI music apps. They clarify remuneration for catalog used in training and, by limiting downloads per user per day, can help stop the flood of AI slop. → Distributors should also act. We welcome TuneCore's recent commitments and continue working with other distributors to flag fraudulent accounts. ISSUE #2: TRANSPARENCY FOR FANS → More than 80% of music lovers want to know when they're listening to AI-generated music. It's simply logical: fans deserve to know whether there's a human behind the song they love, someone they could meet, follow, and see in concert. What we've done: Deezer labels AI tracks from predominantly AI-generated discographies and has removed fully AI-generated songs from algorithmic recommendations. An imperfect compromise, giving fans visibility on "artists" that are mostly AI without penalizing real artists who use AI as one tool among others. What needs to happen next: → As an industry, we must agree on a standard for what gets flagged as AI. Not for its own sake, but to provide clear value to artists and fans. Deezer's ambition is to build consensus on what content should be flagged and how AI songs should be monetized, distinguishing licensed from unlicensed AI. The music industry has weathered every technological shift by adapting, together, always the first cultural industry to do so. AI will be no different, but only if we act as an industry, not as isolated players. That's why Deezer is proposing a pragmatic way forward achieving both goals: safeguarding royalties for human artists and giving transparency to music lovers.

Are you creating amazing 3D models using AI tools like Gemini AI and excited to share them with the world? Hold on for a second! Before you hit that upload button, here are some important Intellectual Property considerations you must keep in mind. 🔸 Who Owns the Rights? When you use AI tools to generate images or 3D models, check the terms of service of the platform. Some tools claim ownership of the generated content, while others may allow you full rights. Always clarify who holds the copyright or license. 🔸 Originality Matters Even if the images look unique, if they are derived from existing copyrighted works, uploading them publicly can lead to infringement claims. Be cautious if you modify existing works. 🔸 Trademark Issues Avoid using recognizable logos, brand designs, or famous character likenesses in your 3D models without proper permission. This can attract trademark infringement problems. 🔸 Right of Publicity If your model resembles a real person, their consent may be required before commercial use. As a Media & IPR lawyer, I always recommend being informed to protect your creativity and avoid legal troubles. Stay creative, but stay safe! #IntellectualProperty #IPR #GeminiAI #3DModels #AIContent #DigitalCreatives #LawTips #MediaLaw #Copyright #Trademark #CreatorsRights

The future of AI in music will not be shaped by lawsuits alone, it will be shaped by the deals we decide are worth accepting today. That is the statement with which I approach the field of AI generated music and I'm glad to see that a few players in this segment are driven by the same ethos. Last month, ElevenLabs introduced Eleven Music, an opt-in model built on licensed partnerships with Kobalt and Merlin. It is not perfect, but it at least showed that AI music can be built on consent and compensation, with a long-term royalty structure built in for the artists whose music is being used. And now, we have Beatoven.ai from India stepping up with their new model Maestro. I know the team, and this is a serious attempt at building something fair, which I first discussed with Mansoor Rahimat Khan in 2023. I am glad that they've been focused on solving this problem since then and are finally in a position to see it through. Maestro has been trained only through licensed partnerships, and more importantly, it is built to deliver ongoing royalty payments to the artists and rights-holders whose work powers the system. With Musical AI’s attribution tech, they can track which elements of a song influence the output and make sure payouts reflect that. That is a big deal, because if you have heard me before, you know where I stand. AI-generated music does not spark joy in me, but I can be remotely ok with it if every person in the chain is credited and paid. What Beatoven and Eleven Music are showing is that there is another path, one where innovation, rights and fairness can co-exist. One where artists are not erased but included and fairly paid. I will always say that these models will not be perfect on day one, and we will need to hold them accountable on transparency, payouts, and execution, but this is the work worth watching. And the fact that a company from India is pushing this forward? That is something I am proud to see. We aren't just consumers of global models anymore, we are building our own, and setting standards the rest of the industry can follow. Godspeed. #MusicIndustry #MusicBusiness #AI #AIGeneratedMusic #Fairplay

✨ For a long time, protecting your IP could only be meaningfully done by playing defense—filing, chasing, notifying, enforcing. But today? Technology is tipping the balance in favor of creators. IP protection and monetization are becoming "design decisions." Baked into the content or embedded in the product. Moving toward enforcement through code, not courtrooms. One example area is attribution: The right to be credited has always mattered—but now, it’s getting serious technical enablement: 🖼️ Content Credentials (Adobe / C2PA) Coalition for Content Provenance and Authenticity (C2PA) Verifiable metadata embedded into the file—creator, edit history, and more. 🔗 Blockchain-Verified Licensing / Legal Terms (MINTangible). IP3 Digital Declare and attach usage rights and restrictions directly to your work - machine-readable (e.g., digital confirmations and compliance). 🤖 On Chain IP Protocols with Attribution + Royalties KOR Protocol / Story Leveraging smart contracts to automatically structure IP assets with downstream attribution. 💧 Watermarking & Fingerprinting (Digimarc, Imatag) Digimarc Tech that sticks with your work, even when copied, cropped, or reposted. 🚫 AI Training Opt-Outs (Spawning.ai) Spawning Let creators decide how (or if) their content trains AI systems. This is more than enforcement—it’s enablement. 📣 What tools or strategies are you seeing that help creators protect their rights and unlock value at the source? #DigitalIP #IPDesign #IP3Digital

UK House of Lords: AI, copyright and the creative industries The UK faces a choice between two futures. In the first, the UK becomes a world-leading home for responsible, licensing-based artificial intelligence (AI) development, where commercial model developers using UK content obtain permission, pay fair remuneration to rightsholders and can deploy their models without questions of legal liability. In this scenario, both the UK’s creative industries and AI sector could thrive. In the second scenario, the UK continues to drift towards tacit acceptance of large-scale, unlicensed use of creative content and long-term dependence on opaque models trained overseas, with most benefits accruing to a small number of US-based firms while harms to UK creators grow. Only the first path is compatible with the UK’s long-term interests. In the age of AI, the protections for creators afforded by copyright are under threat. This is not because the copyright framework is outdated or in need of reform. Rather, widespread unlicensed use of protected works, coupled with limited transparency from AI developers about how their models have been trained, leaves rightsholders unsure about whether their content has been used, and unable to enforce their rights when it has. In addition, the absence of a robust ‘personality right’ or specific protection for digital likeness in the UK means creators and performers are unable to challenge harmful outputs that imitate their distinctive style, voice or persona. Meanwhile, technology sector stakeholders are pressing for the introduction in the UK of a broad new exception for commercial text and data mining (TDM) that would legitimise large-scale AI training on copyright-protected works. Without this, they argue, the growth of the UK’s AI sector will be stunted. There is, however, only limited evidence to show that weakening UK copyright law would significantly expand our AI sector. In contrast, a broad commercial TDM exception presents predictable harms to rightsholders by removing incentives to license protected works for AI training. A new regime must now be created to safeguard creators’ livelihoods, while harnessing the potential of AI for creativity and economic growth. To deliver this, we recommend the following actions: 📍Rule out a new commercial text and data mining exception with an opt-out model 📍Close gaps in protection for identity, style and digital replicas 📍Make transparency about AI training data a statutory obligation 📍Create the conditions for a fair and inclusive UK licensing market 📍Champion the development of technical standards for control, provenance and labelling 📍Prioritise the development and adoption of sovereign AI models

Can Authors Keep Their Work from Being Used to Train AI Without Permission? ✍️📚🤖 If you're a writer, there's a good chance your work has already been absorbed into an AI model—without your knowledge or consent. Books, blogs, fanfiction, forums, articles… All of it has been scraped, indexed, and used to teach machines how to mimic human language. So what can authors actually do to protect their work? Here’s what’s possible (and what isn’t—yet): 🛑 Use “noAI” Clauses in Your Copyright/Terms Clearly state that your work may not be used for AI training. It won’t stop everyone, but it helps establish legal boundaries—and could matter in future lawsuits. 🔍 Avoid Platforms That Allow AI Scraping Before publishing, check the terms of service. Some platforms explicitly allow your content to be used for training; others are more protective. 🖋️ Push for Legal Reform The law hasn’t caught up to generative AI. Supporting copyright advocacy groups and legislation can help tip the scales back toward creators. 🤝 Join Opt-Out Registries Tools like haveibeentrained.com let creators see if their work was used—and request removal from certain datasets. It's not a perfect fix, but it's a start. 📣 Speak Out When authors make noise, platforms listen. Just ask the comic book artists, novelists, and journalists who’ve already triggered investigations and lawsuits. Right now, the balance of power favors the AI companies. But that doesn’t mean authors are powerless. We need visibility. Transparency. Fair compensation. And most of all—respect for the written word. Have you found your writing in an AI training dataset? What did you do? #AuthorsRights #EthicalAI #AIandWriters #GenerativeAI #Copyright #ResponsibleAI #WritingCommunity #AITrainingData #FairUseOrAbuse