frankie-tales

The Artificial Author: copyright and copyleft in the AI era

2026-04-30T11:00:00Z

I recently finished reading Simone Aliprandi’s second edition of a book I would recommend to many people interested in either authoring software and databases or making creative works. ‘The Artificial Author’, as its title suggests, deals with the role of generative AI in creative jobs seen from a legal point of view. This massive book seeks to shed light on a central yet complex issue that, in recent years, has affected many people, not only in the IT sector but also across many sectors of the creative industry.

As often happened in the past, technological changes largely anticipated society's ability to adapt to new developments, particularly in high-impact areas such as copyright, intellectual property, and privacy. Frankly, it is impossible to cover in this review all the aspects of Aliprandi's work that it touches on and explains. Of course, the book is purely informative, but it summarizes where we are currently, from the perspective of legal implications for code and other content generated by AI tooling. The topic is an open problem for several reasons, including the undeniable fact that copyright legislation and the legal systems vary from country to country. So, for instance, what can apply in the US needs to be reconsidered for Europe. Actually, a series of legal trials is still ongoing since the start of the main new AI wave in the autumn of 2022, and some of them have not been finalized or reached an out-of-court (and so off-record) settlement.

That’s significant in common-law systems like the US's, while in Europe, the whole process is much slower and settles based on principles and legislation, not specific trials. The questionable points are:

Is the whole training process for deep learning networks indeed legitimate under the fair use right? In most cases, it seems so, given the current state of things.
Who owns the AI-assisted outputs, and are they copyrightable? The safest answer is: it depends.
Should/could the AI outputs be considered derivative work? Moreover, if legally possible, should it be considered legitimate under any conditions?
If the outputs of independent prompts and refinement are somewhat similar, is there any condition of plagiarism?
Is there really any hope of a clean-room implementation, or even a fully original one, with AI tooling?

While it is clear that a fully AI-created work is not copyrightable under US law, the legal implications of a human-in-the-loop approach remain nuanced at this time. For sure, it seems that a simple prompt is not enough to claim rights on anything, and the whole process should be qualified by a significant human contribution. In the US, the meaning of copyrightable creative works implies involvement of skills, labor, and judgment by a person, and a simple prompt is simply not enough in most cases.

That also explains why, in some contexts, AI tooling should be considered with a grain of salt, and, for sure, the use of any AI-assisted contribution to co-authored/commissioned work should be clearly announced and stated as admitted: it could have future (if not current) impacts on authors' copyright claims.

Specific book sections also address recent European regulatory actions, as well as a general overview of pending and interesting litigations in the field in the European context. Indeed, the EU AI Act deals mostly with privacy.

I strongly suggest that interested people have a deep look at Simone’s book, because I have read too many naive claims here and there on the net about this matter. Of course, what is legally admitted could also be illegitimate or unethical, but prospects and past experiences (even considered in the book) are about future trade-offs and proper alignment, even for aspects such as laws and authors' expectations. For sure, this second edition will not be the last and definitive one for this book: we will see many sentences and regulations written in the near future, and the whole matter is in evolution, but it will still require years to settle.

AI training, copyright and the future of contents creation

2026-01-11T21:00:00Z

I have already addressed the implications of modern LLMs, specifically their training, in the context of copyright and licenses for both code and original content. A 'IANAL' disclaimer applies to this post, but my honest opinion is that such training is a legitimate type of reading and learning after study, unless explicitly excluded in licenses among the licensee's rights.

Following the exploitation of LLMs and the AI boom that began in 2022, several lawsuits and litigations emerged among multiple parties, with a few reaching a significant milestone through the first court rulings. Note that every country has a bit different regulations about copyright and fair use, so the current lawsuites could be only the starting point of a long list of legal actions.

While most of the current lawsuits seem to demonstrate that Anthropic or Meta had the right to use books bought (in paper or digital form) for LLMs training (on the basis of the fair use principle), the most problematic aspect instead is the apparent use of pirated books taken from LibGen and other known piracy websites, which - if confirmed - can result in potentially destructive damange for the companies, to compensante authors and pay fees in the order of hundreds of billions.

The same problems are present in the coding parts: again, using FOSS-licensed code for training could fall under fair use, but training using private codebases, as well as proprietary ones, could be equally destructive for the same companies, as well as for GitHub and Microsoft. The key point would be demonstrating, without any doubt, the unfair use of private or pirated content, of course.

Of course, I'm quite sure future licenses for FOSS codebases and documentation could include an explicit exclusion clause for AI training, which could jeopardize the legitimation of use even for future FOSS code. I would expect such a license change, as some projects already explicitly exclude AI-based contributions. My opinion about such a question is that it could represent shooting oneself in the foot, due to the pervasivity of AI tools among developers currently. Adoption of AIAD could represent a boost in development time if adopted with a healthy dose of skepticism (i.e., a human-in-the-loop approach). About that, I'm quite convinced of Linus Torvald's point of view: the point is not who writes the code, but who is technically responsible for it and ensures the required quality review and supervision.

Moreover, an implication of the current polarization in the AI hype is the future (present?) crisis of traditional web content providers. A symptomatic case is the StackOverflow crisis, which will, with high probability, lead to the end of the service as we know it in the near future.

That will have an impact on future AI training, too, for sure, because SO has been for years a huge source of knowledge about multiple fields in IT. What if fewer and fewer people will contribute to Wikipedia and general web content? What if more and more sources of information were to reserve the right to use their information for pure human-driven study? Knowledge has not been static in human history; AI models will need to continuously enrich their training sets and stay up to date.

It would be grotesque if the whole AI hype were brought to a halt by such copyright-based legal questions (even if I'm pretty sure a fully fair training would be possible now for such companies, who knows the impact of a more limited approach on the final result?). Surely, this seems the most serious threat to the future of such companies and the whole AI-based solutions.

The only true solution to such a threat is finally having a true open training model, which details sources and the whole process of training with full transparency, something that even the so-called open AI models are still far to be ready to provide.

References

Again about AI, copyright, uses and abuses

2025-04-29T20:00:00Z

My last post dealt with some ideas about copyright that need more in-depth analysis. First, as it was common in the old good days, IANAL applies to this post and the whole topic. The final results of current litigations in courts that touch on some of the primary companies involved in the whole AI thing could ultimately differ from what is now the common sense point of view (mine). This post could become rapidly obsolete, so another disclaimer is due for this aspect, too.

A nice summary of the matter in the summer of 2024 can be read in [1].

I have to partially fix the assertion I made about copyright by Anthropic/Google and whatever company in a certain sense. At least for US copyright law, anything directly produced by non-humans is (or seems currently) not admissible for copyright coverage [2]. As you know, there are a few differences between different jurisdictions, specifically the US and the rest of the world. That's the reason why, indeed, Joseph Borg and Galyna Podoprikhina by WH Partners [3] say:

Despite the "common" belief that a work can be only be
protected by copyright if it is created by a human, one must
bear in mind that copyright laws are not uniform around the
world, especially when it comes to AI-generated work, or a
work created with the assistance of AI. Currently, apart
from the UK as described above, AI artwork is also subject
to copyright in Ireland, India, and New Zealand.

If a photo taken with a camera is admissible for copyright, the same could be claimed for any other tool, one could say. In the past centuries, even photography would have been indubitably not initially admitted to copyright and the artistic scene: indeed, a painting is a lot different from a photo. A modern photographer would not agree with such mortification of her/his work. Today, the reality is quite different, and photography is fully accepted among modern arts, with all implications about intellectual property and copyright.

Most authors agree with the significance of the human contribution to the work to define a copyrightable work, but how this contribution could be quantified is obscure. The number of prompts and replies, as well as the size of context contributions, are significant and sufficient efforts, or should they be quantified in LOCs and the size of direct patches to the AI artifacts? And in that case, what is the percentage of human-driven contribution that represents the threshold for deciding if the work is copyrightable or not? I'm afraid that the final verdict is something to decide in a court, as in cases of plagiarism.

But for AI, for ages, we also had RAD and no-code/low-code utilities that seemed the future of development for specific applications, with all their limitations (not too different from AI ones, to be fair). Even in those cases, copyright claims could be problematic.

That said, there is also the problem of training possibly performed without authorization. While most of the FOSS software is covered by one of the OSI licenses, not all licenses are compatible with each other, so the resulting LLM model is questionable and possibly unfair. I will ignore, for decency, the eventual use of proprietary content for the purpose of training, which already seems to be the subject of lawsuits by multiple parties in some contexts: see for instance [4] and [5].

As written in [2], the final destination of the whole topic is still foggy and unclear. Multiple parties are involved, and a series of lawsuits and claims are pending. This seems to be the reason why some companies explicitly deny the possibility of using AI tools in their developers' daily work. Due to their pervasive diffusion at multiple levels, this becomes increasingly difficult to avoid. As often in the past, tools are still forward than rules and sh*t could happen in a not so far future.

References

AI artifacts, copyright and electric sheep dreaming

2025-04-22T12:30:00Z

My last post captured the attention of my old fellow Sandro 'strk' Santilli on Mastodon, who sent a provocation about the whole AIAD thing. So, the challenge is accepted.

First and foremost, the whole AIAD issue is a complex and hotly debated topic. The question of whether the training practices of the past and present should be considered fair use is a matter of contention. This is particularly true for existing code bases on well-known repositories and other types of content, a complexity that keeps us all intellectually engaged.

In the specific case of code, the basis for deciding about such a question was formerly stated in the accompanying licenses. Honestly, there is nothing in the current formulation of OSI licenses (BSD and GPL, among others) that precludes such a very special activity. The non-discriminating conditions apply to any human activity, and the neural net training - undoubtedly a human-driven task - is not excluded. The training is a learning type of activity or a sort of.

That said, the NN training is, of course, a massive and intensive kind of learning. But let me consider any artifact created by an AI model as a direct derivative product (this is a forcing of the concept for me, but let me axiomatically accept it). That could be an 'original' product created from scratch (i.e., by direct prompting or by limited documental context) or a direct derivate one (because it is based on previous code proposed as part of the AI context). Suppose you ask any of the big models for a direct clarification of the terms of use. In that case, you will discover that any of their artifacts retain the copyright ownership of the product, but there is a very permissive use that perfectly adheres to the four fundamental freedoms of FOSS licenses. Of course, the results could be based on a previous code base. In that case, its license still applies for such a derivative work, even with the additional copyright of Anthropic, Google, or OpenAI.

This could pose a significant problem if the final software product needs to retain one specific copyright holder, as is the case with most proprietary software or some FOSS ones. Understanding the potential impact of copyright transfer is a key consideration in this context, and it's crucial for us to be fully informed and aware.

Is the process of an AI participating in development really that different from what any average hacker does when participating in FOSS projects? I don't think so. You add your copyright to the existing ones for the parts that are under your direct control and accept conditions of use already defined in a license. The true challenge, in my humble opinion, lies in the changing of licenses. Any ex-post change should start with a note of acceptance by all copyright holders, including Anthropic, Google, etc.

I consider this difficulty a feature, not a bug. I advise against participating in projects with copyright transferring because of the potential for changing the license later when your contributions move out of your control. It happened in the past, and it will happen again.

One should also consider the licenses of many other sources of knowledge and inspiration for developers. How many people know that the default license for StackOverflow code snippets is CC BY-SA? Indeed, how many developers actually add an acknowledgment in their software for such snippets? Or even for snippets taken from sites, blogs, books, or manuals without considering that such sources are even more restrictive for use and creating derivative work? Isn't our full daily work the result of a long learning phase, based on our education and training by books, experiences of others, as well as trial-and-error processes?

That said, let me spend some words about the elephant in the room. In the context of AI and copyright law, the 'elephant' represents the complex nature of AI models and their potential to create original works. Do AI models dream of electric sheep? Well, I don't think current models are pure stochastic parrots, to be honest. I think there are probably dozens or hundreds of cognitive forms that govern what we call generically intelligence, including some emotive and empathic forms that one can also find in a dog, a cat, or a dolphin. One of those forms is probably captured from the neural network model of functional representation, which is also perhaps in common with part of our mind. We are at an average level, much more effective and efficient in those regards, and to be fair, I would also say that hallucinating is a common experience even on the human side. We are much more complete and retain contexts that are wide, like a lifetime. A few people are terrified by this observation and seek refuge in negation or faithful certainties.

We are complex organisms with probably still partially known processes that govern our so-called intelligence, which is physically based on cells and energy in our brains, whether we like it or not. We found a way to mimic part of this complex process, with all limitations of the case. Is this intelligence? I don't know, but at the end of the day, what is intelligence? When none asks me about that, I know perfectly what is, but if you ask me about that, I don't know anymore.