Copyright Clearance Center (CCC)

⁉️ What guidance can you provide for someone who wants to use copyrighted material for internal use only? For insight on today's copyright in medical communications question from CCC's Chloe Olore, head to the community: https://lnkd.in/em6j9Xsz #Copyright #MedComms

This past summer, Copyright Clearance Center (CCC) hosted “Camp OA,” a series of community forums where publishers dove deep into technology, publishing models, and workflows. Like the summer camps of our youth, it was time set aside for learning and development—while building meaningful relationships.  Shannon Reville writes about four of the most memorable takeaways in our latest Velocity of Content blog post 👇 https://lnkd.in/dH5mN8h5

As lawsuits mount against AI companies over copyright infringement – most recently with Dow Jones and NY Post suing Perplexity AI for “massive illegal copying,” Microsoft’s CEO Satya Nadella has called for more flexible copyright laws. For more, check out our latest Copyright Clearance Center (CCC) Velocity of Content blog post via the link 👇 in which Roanie Levy writes on, "Learning from Textbooks vs. Training AI: Why Nadella's Analogy Fails." https://lnkd.in/eXYEyDGK

This week's MedComms insight is provided by Leslie Lansman, based on this question: "If you would like to re-use a figure/table for a publication that is available via an Open Access (OA) article, but that figure/table has been reused with copyright permission from an original paper, do you then need to request copyright permission to reuse the figure from the OA article from the 'original paper'?" Head to the Copyright in Medical Communications Community for complete insights: https://lnkd.in/em6j9Xsz #Copyright #MedComms

Anthropomorphic Bias Clouds the Fair Use Debate Around AI Training Just read a fascinating paper by Jacqueline Charlesworth (https://bit.ly/3YpBqvU) that crystallizes something that's been making me increasingly uneasy - our anthropomorphic bias when it comes to AI systems. This natural tendency to attribute human characteristics to non-human entities makes it particularly challenging to discuss AI systems accurately. When we use terms like "learning" and "training" for AI, it subtly leads us to think these systems process information like humans do. This anthropomorphic bias enables flawed arguments about fairness - like Satya Nadella's recent textbook analogy (http://bit.ly/3Unpn0D) which I recently wrote about (https://bit.ly/4eWiR9o). Charlesworth's paper brilliantly illustrates why this bias is particularly dangerous in copyright discussions. These systems don't "learn and discard" content - they encode, store, and exploit works for their expressive value through complex algorithmic processes. I think of it as a sophisticated search and retrieve library where the books haven't been discarded but rather transformed and embedded within the system's neural networks. The paper explains why previous fair use cases like Google Books don't support AI companies' current practices. Those cases involved copying to facilitate specific objectives like searching, indexing, or interoperability. What AI companies are doing now is fundamentally different - they're copying works wholesale to exploit their expressive content. An important read that helped clarify my own thinking about how the language we use shapes these critical debates about AI, copyright, fairness and more. #AI #Copyright #FairUse #CreatorRights https://lnkd.in/g4hgqxbv

In our latest Copyright Clearance Center (CCC) Velocity of Content blog post, Mark Seeley writes, "How Pirate Websites Undermine Research Integrity." This piece originally appeared in Research Information and is republished with permission. Click the link 👇 to read the full analysis. https://lnkd.in/eyayNDs3

Publishers Lunch writes about the Copyright Clearance Center (CCC) panel at Frankfurter Buchmesse Let Licensing Bloom: The Root of Responsible AI, where panelists "discussed their concerns about AI technology and the value of licensing content in order to protect it. Click the link 👇 for more. https://lnkd.in/g5CknBXx

AI-driven research often relies on vast amounts of information, much of which may be sourced from published literature. This content is vital for training AI models and driving innovation. However, using copyrighted material creates a complex set of legal and ethical considerations. This is where a literature management solution that integrates rights verification for AI uses becomes indispensable. Adopting this type of literature management solution lays the foundation for responsible AI development, where content utilization is aligned with legal standards and ethical business practices across the organization. As organizations increasingly leverage AI to fuel research and innovation, a key challenge is confirming that the content is properly licensed and the associated rights are respected. The need for a robust, compliant, and efficient approach to managing copyright has never been more critical. For more, click the link 👇 for the latest Copyright Clearance Center (CCC) Velocity of Content blog post by Madison Tremblay https://lnkd.in/gpinvCDG

A critical analysis of Microsoft CEO Satya Nadella misleading comparison between human learning and AI training. This post examines why the "reading textbooks" analogy fails to capture how LLMs actually work and their potential impact on content creators, while highlighting the need for balanced solutions that serve both AI innovation and creative rights. #ArtificialIntelligence #Copyright #AIEthics #ContentCreators #DigitalRights #Innovation #TechPolicy #AIRegulation #CreativeEconomy #FairUse #LLMs #IntellectualProperty #AIGovernance #TechInnovation #DigitalFuture