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Creativity, The Fifth Freedom & Access To Knowledge
from the freedom-of-knowledge dept
This series of posts explores how we can rethink the intersection of AI, creativity, and policy. From examining outdated regulatory metaphors to questioning copyright norms and highlighting the risks of stifling innovation, each post addresses a different piece of the AI puzzle. Together, they advocate for a more balanced, forward-thinking approach that acknowledges the potential of technological evolution while safeguarding the rights of creators and ensuring AI’s development serves the broader interests of society. You can read the first, second, third, and fourth posts in the series.
In April 2007, Janez Potočnik, then European Commissioner for Science and Research, introduced the concept of the Fifth Freedom: the “freedom of knowledge.” His vision was simple but ambitious—enhance Europe’s ability to remain competitive through knowledge and innovation, the cornerstones of prosperity. Fast forward to today, the momentum for this Fifth Freedom is building once again, with both the Letta Report and the Mission Letter of the new EU Commissioner for Startups, Research, and Innovation emphasizing its significance.
But how does this freedom of knowledge intersect with creativity and copyright?
AI, Learning, and the Limits of Copyright
Machine learning (ML) systems learn in a way strikingly similar to humans—by observing and copying. This raises an important question: should ML systems be allowed to freely use copyrighted materials as part of their learning process? The answer is not just about technology; it goes to the heart of what copyright law aims to protect.
Traditionally, copyright protects the expression of ideas, not the ideas themselves. This is an important distinction because it allows others to take inspiration, innovate, and build upon ideas without infringing on someone else’s creative output. When an ML system is trained, it doesn’t care about specific creative choices—like the lighting or composition of a photo. It just wants to learn the underlying pattern, such as recognizing a stop sign. Similarly, a natural language model uses written text not because it appreciates the author’s unique writing style, but because it needs to learn the structure of language.
Humans also do this all the time. We often replicate expressions when learning, but our goal is not to plagiarize someone’s unique creative touch—it’s to grasp the idea behind it. This concept is embedded in many legal precedents. For instance, in the American Geophysical Union v. Texaco case, photocopying was used not for the beauty of the prose, but simply as a convenient way to access scientific ideas. Similar issues arise in cases about software interoperability, functional objects like clothing designs, and even in disputes over yoga routines. Copyright should protect creative expression—not the ideas, facts, or functional elements that underpin them.
Why This Matters for Machine Learning
This distinction is particularly important for ML. If we allow copyright law to get in the way of machines learning from data for purely non-expressive purposes, we’re potentially hampering technological advancement. Allowing ML systems to copy for learning—without trying to replicate the creative aspects of the original work—is essential for innovation. This is not just a matter of advancing technology but also of staying true to the spirit of copyright law, which is meant to balance the interests of creators and the public good.
However, as Professor Lemley has pointed out from a U.S. law perspective, the freedom for ML to learn should have limits. If an ML system is being trained to create a song that mimics the style of Ariana Grande, it’s no longer just about learning—it’s about copying a creative expression. In such cases, the question of whether it qualifies as fair use becomes much tougher. Yet, even here, it’s crucial that copyright doesn’t end up controlling unprotectable elements like a musical genre or a broad artistic style.
Finding the Balance: Innovation and Protection
The concept of the Fifth Freedom—freedom of knowledge—cannot thrive if copyright is used to restrict learning and innovation. We need a balanced approach: one that protects the hard work of creators, while ensuring that copyright doesn’t stifle the fundamental right to learn, innovate, and build upon existing knowledge. This is especially relevant now, as AI and machine learning shape the future of creativity and the knowledge economy in Europe. If we get this balance right, we can ensure that both creativity and innovation continue to flourish in the digital age.
Caroline De Cock is a communications and policy expert, author, and entrepreneur. She serves as Managing Director of N-square Consulting and Square-up Agency, and Head of Research at Information Labs. Caroline specializes in digital rights, policy advocacy, and strategic innovation, driven by her commitment to fostering global connectivity and positive change.
Filed Under: access to knowledge, ai, copyright, creativity and ai, fifth freedom, freedom of knowledge, knowledge, machine learning
Comments on “Creativity, The Fifth Freedom & Access To Knowledge”
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Meh, doesn’t matter. AV, chatcontrol and censorship are ripping the web apart.
Oh, it’s you again.
Freedom of knowledge would suggest tearing out the regurgitation engines root and branch before they poison our collective knowledge base any more than they already have.
Or just be done with copyright. Copyright and trademark have become nothing more than tools to maintain monopolies and harm the public for the benefit of the wealthy.
People have been jailed for pirating or sharing pirated content, and right now it’s looking likely that meta will get off with purposely pirating content because it didn’t want to pay people.
Meanwhile anyone not of such a large corporation with millions and billions, faces bankruptcy or has to simply give up if they are so much as accused and threatened with a lawsuit.
(Your regular reminder that the blurb at the bottom is a euphemism for lobbying, evaluate accordingly)
The tricky part is that generalist ML systems are often capable of both. OpenAI’s 4o system can be asked to do something in the style of Studio Ghibli, despite not being trained exclusively to do that sort of thing. The model might not “care” about a particular style, but that style is baked in, just waiting to be pulled forth with the right prompt. In more egregious cases, you can pull the training data out more or less verbatim.
There’s no clear way to draw a distinct line on a model being trained to mimic a style, or for more general purpose. And it’s difficult (or impossible in the case of open models) to regulate it at the prompt level.
OpenAI has already crossed the murky line where it’s model technically doesn’t allow you to request a specific style as a fig leaf, yet it aggressively advertises using examples clearly poaching off Ghibli with a nudge, nudge, wink, wink.
I’m sure that copyright limits creativity- that’s why this site exists- but AI is not the way to go about bringing back ideas. What does a machine that generates sloppy artwork which displaces actual artists, or spits out hopelessly generic text is ever going to know about publishing important works or essays that readers need to know so badly, that the original author eas arrested
Every time I see the new post from this series, I half-expect the next to be “oh yes, we were just joking, testing you if you can figure out that we were messing with you”.
But hey, I’m new here.
I was writing a very lengthy comment about the effects AI has on the value of art and creativity in the world where attention has became a commodity, but then I looked at it and realized it’s written in a quite “eloquent”, literary way. AI could write like that. So I erased it and then kind of sidetracked.
What if people start to think that the ones who put a lot of effort into their writing or drawing all use AI?
I was once accused by a person that a drawing of mine is AI because it was “too perfect.” It’s not only AI being used to “rip off” things made by people. The very existence of content made by a machine puts a shadow of doubt on everything we encounter. I think this is the scariest part when it comes to the presence of generative AI in the creative field.
Now I’m imagining the future:
People write poorly written comments, articles and books with grammatical errors in there on purpose to prove their humanity. When there is enough material on the web, AI slowly starts mimicking that. At this point, nothing is proven to be real. Maybe they’ll just turn their appliances off at this point and go outside to “touch grass” as some people say nowadays.