Martensen IP Offers Critical Guidance on AI Intellectual Property Risks, Examples of Copyright Issues, and FAQs
AI Copyright Infringement: Understanding AI Copyright Law, Training Data, Fair Use, and Legal Risks
Colorado Springs, CO, Oct. 17, 2025 (GLOBE NEWSWIRE) -- 
Artificial intelligence (AI) tools such as ChatGPT, Google’s Gemini, Anthropic’s Claude, and other large language models (LLMs) have rapidly shifted from novelty to necessity. They now assist with drafting articles, summarizing research, generating marketing copy, writing software code, and even brainstorming creative works. Yet, as their use spreads, a pressing question has emerged: When an AI system generates text, images, code, or music, could it be infringing a copyright?
The answer is complicated. AI models are trained on vast datasets that include copyrighted works. They do not simply copy and paste, but under certain conditions, they can produce material closely resembling protected works. That possibility raises legal and ethical concerns for businesses and creators alike. For attorneys who advise companies on safeguarding intellectual property (IP), understanding these risks is critical.
This article explores how AI systems use copyrighted material, what U.S. copyright law says about derivative works, how courts are approaching these questions, and what steps both businesses and legal teams can take to reduce risk.
How Large Language Models Are Trained and Why That Matters
To understand copyright risk, one must first grasp how modern AI systems learn. Large language models and other generative AI tools are built by ingesting massive volumes of data. These datasets often include public web pages such as Wikipedia and news outlets, public domain works, licensed content, open-source code repositories, and user-generated material from forums and Q&A sites. Inevitably, some of what they absorb is copyrighted.
The training process does not mean the model stores literal copies of everything it reads. Instead, it creates a complex mathematical map of patterns—statistical weights and probabilities that help it predict the next word in a sentence or the next pixel in an image. Nevertheless, if a piece of content appears frequently or is highly distinctive, the model can “memorize” it. Researchers have shown that large models sometimes reproduce code snippets, poetry, or paragraphs nearly verbatim. This phenomenon, sometimes called “regurgitation,” is relatively rare but not negligible.
This dynamic is crucial for lawyers and IP professionals. A model trained on copyrighted material without permission may later generate outputs so close to the original that they constitute infringement. Even if the training itself were eventually found to be lawful, the output could still create liability if it reproduces protected expression.
Copyright Law Basics and the Concept of Derivative Works
Copyright protects original works of authorship fixed in any tangible medium—books, articles, software code, photographs, music, films, and more. It does not protect ideas or facts, but rather the particular expression of those ideas. The copyright owner alone holds the right to reproduce the work, prepare derivative works, distribute copies, and publicly perform or display the work.
A derivative work is one that recasts, transforms, or adapts a preexisting work into something new—for example, a sequel novel, a movie based on a book, or a remix of a song. If AI-generated content qualifies as a derivative work of someone else’s protected expression, distributing or selling it could constitute infringement.
Fair use provides an important exception. U.S. law allows limited use of copyrighted material without permission for criticism, commentary, news reporting, teaching, scholarship, and research. Courts analyze several factors: the purpose and character of the use (especially whether it is transformative or commercial), the nature of the copyrighted work, the amount used, and the effect on the original’s market.
While some scholars argue that training AI models is transformative and thus fair use of copyrighted material, this is not settled law. More importantly, even if training is fair use, outputs that substantially copy protected text or images are not automatically shielded.
The Unsettled Legal Status of AI Training and Outputs
Whether training a model on copyrighted data is legal remains one of the most pressing questions in IP law. Some argue that using copyrighted works to train an algorithm is transformative because the model learns patterns rather than storing copies, and because the resulting product serves a different purpose than the original material. Others counter that training involves reproducing entire works for commercial gain and could undermine the market for those works.
Several lawsuits aim to clarify this debate. The Authors Guild has sued OpenAI, claiming its models reproduce excerpts from their books. Getty Images has sued Stability AI, arguing that its photos were scraped and used to train image generators. Artists such as Sarah Andersen have brought actions claiming that image generators copy their work and style. The New York Times has filed suit against OpenAI and Microsoft, alleging that their models can output articles nearly word-for-word.
So far, courts have not issued a sweeping decision that settles whether training is fair use. For now, companies must operate in a gray area.
Another open question concerns whether AI outputs themselves can be copyrighted. The U.S. Copyright Office has made clear that purely machine-generated works without human authorship are not eligible for protection. If a user provides only a simple prompt and accepts the output without modification, they may not own the copyright. But if the user exercises meaningful creative control—editing, directing, or shaping the output—their contributions can be protected.
That distinction matters because users might be unable to claim exclusive rights to unedited AI-generated material. Yet at the same time, they could still face liability if the output infringes someone else’s rights. In other words, you may not own the AI-generated work, but you could still be sued for publishing it.
AI Intellectual Property Risks: Real-World Scenarios Where Copyright Infringement Can Arise
These legal nuances translate into concrete risk scenarios.
- A marketing team might use ChatGPT to write a blog post, only to find that parts of it closely match an existing copyrighted article.
- A developer could accept code from an AI assistant that reproduces licensed or proprietary snippets, inadvertently violating a license.
- An artist might generate an image that imitates the protected style or even the distinctive composition of another creator.
- Companies fine-tuning their own models on internal or third-party data may inadvertently incorporate protected manuals, reports, or images, later generating outputs that violate contracts or IP rights.
For most everyday writing tasks, the risk is low but not zero. AI tends to paraphrase rather than copy. But accidental reproduction can occur, particularly with widely circulated works or with prompts that explicitly ask the AI to mimic a specific source. In high-stakes contexts—software development, commercial art, and corporate publishing—the consequences of infringement could be significant. And as the capability of AI continues to evolve, so do the risks.
How Derivative Works Risk Plays Out in Practice
The distinction between copying ideas and copying expression is critical. Copyright law does not stop someone from writing about a wizard school, but it does forbid reproducing J.K. Rowling’s specific wording from Harry Potter. An AI asked to “write a fantasy story about a young wizard” will be safe. An AI told “write the first chapter of Harry Potter and the Sorcerer’s Stone” might produce something infringing.
Visual art introduces a murkier question: Can a style itself be protected? Generally, copyright covers specific works, not general artistic styles. Yet, some artists are suing AI companies for style mimicry, arguing that it undermines their market. Courts have not conclusively answered whether closely imitating a living artist’s style is infringing, but the risk is rising as these lawsuits proceed.
The concept of “transformative use” also looms large. Courts may ask whether the AI output merely repackages protected work or truly transforms it into something new. If an AI rephrases an article but keeps its structure and unique turns of phrase, the risk increases. If it uses the article only as raw material to create something novel with a different purpose—for instance, statistical analysis or satire—the risk decreases.
Unintentional Copyright Infringement and AI Copyright Lawsuits
Under U.S. copyright law, infringement does not require intent. A company that publishes AI-generated material can be held liable even if it believed the work was original or if it had no reason to suspect copying. For example, a marketing team could use ChatGPT to write an article that happens to reproduce portions of a protected text. If that content is published or monetized, the copyright holder could bring a claim regardless of whether the team knew about the infringement.
This risk is amplified by the opacity of AI training data. Users typically have no insight into the sources the model has seen or the probability that certain outputs might closely mirror protected works. Even prompts that seem safe—such as asking for a technical explanation or a product description—can yield language taken almost verbatim from a copyrighted source.
Businesses relying on AI without legal review may also be exposing themselves to reputational damage and costly litigation. Courts can award statutory damages for infringement even when it is accidental, and the financial impact can be significant. Moreover, claiming that AI created the content does not absolve the user, because the person or entity that publishes or profits from the work is generally responsible for ensuring it does not violate intellectual property rights.
To mitigate these risks, organizations should adopt proactive review and vetting processes, similar to how they handle content from freelancers or third-party contractors. Plagiarism detection, legal review of high-profile publications, and clear policies around AI use can help reduce the likelihood of accidental infringement. Education is also critical: Employees should understand that AI tools do not guarantee originality and that responsibility ultimately falls on the user.
FAQs
Can AI-generated content infringe copyright?
Yes. Even though AI tools like ChatGPT or image generators don’t intentionally copy, they may reproduce copyrighted text, code, or images. If the output closely resembles a protected work, publishing or using it could count as infringement.
Who owns the copyright to AI-generated works?
In the U.S., purely machine-generated content without human authorship is not eligible for copyright protection. If a user edits or significantly shapes the output, their creative contributions may be protected, but simple prompts usually are not enough. So, understanding AI-related intellectual property risks is essential.
Is training AI on copyrighted material considered fair use?
This is unsettled law. Some in the AI fair use debate argue that training is transformative and thus falls under fair use; others contend it copies entire works for profit. Several lawsuits are underway, and courts have not yet provided a definitive ruling.
What are some examples of AI copyright infringement risks?
Businesses face several AI-related copyright infringement risks. For example, a generative AI could produce a blog post that is substantially similar to a published article or create code that improperly reuses licensed snippets. In the art world, a model might generate images that unlawfully copy a living artist’s unique and recognizable style. Furthermore, a significant underlying risk involves the AI models themselves, as corporations could face liability for training their systems on vast amounts of third-party copyrighted data without permission.
How can businesses reduce copyright risk when using AI?
To reduce copyright risk when using AI, businesses can implement several key strategies. It's crucial to educate employees about copyright liability and fair use principles, while also training them to avoid prompts that ask AI to mimic specific creative works. Additionally, companies should run plagiarism checks on AI-generated outputs and have their legal teams review any high-profile publications before they are released.
What lawsuits highlight the AI copyright issue?
Notable cases include:
- The New York Times v. OpenAI and Microsoft (news content)
- Getty Images v. Stability AI (photographs)
- Authors Guild v. OpenAI (book excerpts)
- Sarah Andersen v. Stability AI (art style imitation)
Protect Your Organization From Generative AI Copyright Issues
Now, you have a solid foundation for understanding AI and copyright issues. For strategies to protect your organization’s intellectual property and minimize liability when using AI tools, look for our article “A Practical Guide to Managing Generative AI Copyright Risk.”
If you have specific questions about your organization’s use of AI or need legal guidance, our team at Martensen can help. Contact us today.
About Martensen IP
At the intersection of business, law and technology, Martensen understands the tools of IP. Martensen knows the business of IP. We understand the tech market, especially when the government is a customer, and we know how to plan, assess, and adjust. Patents, trademarks, copyrights, trade secrets, licenses are our tools.
Martensen IP Media Contact
Mike Martensen | Founder
(719) 358-2254
Legal Disclaimer:
EIN Presswire provides this news content "as is" without warranty of any kind. We do not accept any responsibility or liability for the accuracy, content, images, videos, licenses, completeness, legality, or reliability of the information contained in this article. If you have any complaints or copyright issues related to this article, kindly contact the author above.
