GPT-4o: Arguments equating LLM training to human learning or defending practices like copy-pasting and sampling as lawful fail when subjected to legal and technical analysis.

This essay by ChatGPT has been unedited and therefore can contain mistakes and hallucinations and is based on LinkedIn comments available here and here.

The Distinction Between LLM Training and Human Learning: A Legal and Technical Perspective

by ChatGPT-4o

The rise of large language models (LLMs) has sparked debate over the ethical, legal, and technical implications of using massive datasets of human-generated content to train artificial intelligence systems. Among the arguments put forth, some claim that LLMs mimic human learning or that practices such as copy-pasting and sampling are lawful due to their necessity for AI development. However, a closer examination reveals that these claims fail under legal, technical, and ethical scrutiny. Furthermore, specific legal precedents highlight the challenges these arguments face when tested against copyright law.

1. Human Learning vs. LLM Training: A Misguided Analogy

One widely circulated argument equates LLM training to human learning. Advocates suggest that, just as humans learn by absorbing and interpreting information, LLMs should have similar freedom to process vast amounts of copyrighted content. However, this analogy collapses under legal and technical scrutiny.

Legal Distinction:
Human learning involves creative engagement with information. Humans synthesize, interpret, and often transform the knowledge they acquire, applying it in unique contexts. LLMs, on the other hand, statistically model patterns in the data they process, often retaining the capacity to reproduce that data verbatim. Copyright law, as established in cases like Authors Guild v. Google, emphasizes that transformative use is central to fair use. Transformative use occurs when new expression, meaning, or message is added to the original work, which is typically absent in LLM training processes.

Technical Distinction:
The inner mechanics of human cognition differ fundamentally from the algorithmic functioning of LLMs. Humans inherently filter, adapt, and apply information creatively, whereas LLMs rely on vast computational resources to encode and model patterns within data. These systems lack comprehension and context, increasing the likelihood of reproducing protected works verbatim during inference. This risk is highlighted in technical audits of AI models, where outputs often reflect significant portions of the training data.

Ethical Concerns:
Unlike humans, LLMs process data on an unprecedented scale, ingesting billions of texts without the natural limitations of human memory or comprehension. One expert perspective underscores that this disparity exacerbates ethical and legal concerns, particularly when proprietary or copyrighted works are used without authorization.

2. Copy-Pasting and Sampling: Legality in Question

Another argument posits that practices such as copy-pasting and sampling, which are intrinsic to AI training, are lawful. Proponents assert that such processes are necessary for building functional and effective AI systems. However, this reasoning oversimplifies the legal framework governing copyright.

Copy-Pasting and Copyright Infringement:
Copy-pasting—replicating segments of text verbatim—constitutes copyright infringement unless explicitly authorized or justified under fair use. In Anderson v. Stallone, the court ruled that partial reproduction of copyrighted material, even as part of a new work, could qualify as infringement. Similarly, LLMs trained on and reproducing verbatim excerpts of copyrighted content without authorization face significant legal risk.

Sampling and Fair Use:
Sampling, or extracting snippets of copyrighted text for training, is not automatically fair use. The four-factor fair use test—examining purpose and character of the use, nature of the copyrighted work, amount and substantiality of the portion used, and the effect on the market—provides a framework for analysis. Commercial LLM developers often fail this test due to the profit-driven nature of their use, the extensive scope of data ingested, and the potential market harm caused to original creators.

For example, in Campbell v. Acuff-Rose Music, Inc., the court established that transformative use can favor fair use, but commercial motivations and market substitution weigh against it. The large-scale, unlicensed ingestion of copyrighted text by LLMs undermines this balance, as it competes with the licensing market for creative works.

Precedents in Music Sampling:
The legal treatment of sampling in the music industry further challenges the claim that sampling in AI is lawful. In Grand Upright Music Ltd. v. Warner Bros. Records Inc., the court held that unauthorized sampling, even of short snippets, violated copyright. This precedent underscores the principle that all forms of unlicensed reproduction, regardless of length, require authorization.

3. Misconceptions About Transformative Use in AI Training

Advocates for AI often claim that LLM training is inherently transformative or results in derivative works. This misrepresents the legal standards for both concepts.

Transformative Use and Derivative Works:
In copyright law, a derivative work must add significant new expression, meaning, or value to the original. Transformative use, as articulated in cases like Sony Corp. of America v. Universal City Studios, Inc., requires that the new use alter the original work in a meaningful way. LLM training, which statistically processes data and often reproduces patterns without meaningful alteration, fails to meet these criteria.

Economic Harm and Market Substitution:
The large-scale use of copyrighted content in LLM training without licensing displaces market opportunities for creators. This was emphasized in Harper & Row v. Nation Enterprises, where the court found that unauthorized use undermining a work's economic potential weighed heavily against fair use. Similarly, unlicensed LLM training diminishes licensing opportunities, disrupting the delicate balance that copyright law seeks to maintain.

4. Technical and Regulatory Solutions

Preventing Verbatim Reproduction:
LLM developers can implement safeguards to limit verbatim reproduction of training data, such as post-processing filters and dataset audits. Failure to adopt these measures reflects negligence rather than technological inevitability.

Transparent Licensing and Data Sourcing:
Advocates for creators argue for greater transparency in training datasets and ethical data sourcing. Licensing agreements for copyrighted material can ensure compliance with copyright law, foster trust among stakeholders, and align with ethical AI development principles.

5. Broader Implications for Creative Industries

Unregulated use of copyrighted content for LLM training threatens to destabilize creative ecosystems. Normalizing unlicensed use diminishes the economic incentives for content creation and risks concentrating power in the hands of a few AI developers. By adopting a balanced regulatory framework that respects creators' rights, it is possible to encourage innovation without undermining the foundational principles of intellectual property law.

Conclusion

Arguments equating LLM training to human learning or defending practices like copy-pasting and sampling as lawful fail when subjected to legal and technical analysis. Key legal precedents, including Authors Guild v. Google, Anderson v. Stallone, and Grand Upright Music Ltd. v. Warner Bros. Records Inc., highlight the importance of transformative use, licensing, and the economic implications of unauthorized reproduction. To advance AI responsibly, a robust framework that combines licensing, transparency, and technical safeguards is essential. Such measures will protect creators, preserve incentives for innovation, and ensure that the transformative potential of AI is realized within the boundaries of copyright law.