Artificial intelligence and copyright

In the 2020s, the rapid advancement of deep learning-based generative artificial intelligence models raised questions about whether copyright infringement occurs when such are trained or used. This includes text-to-image models such as Stable Diffusion and large language models such as ChatGPT. As of 2023, there were several pending U.S. lawsuits challenging the use of copyrighted data to train AI models, with defendants arguing that this falls under fair use.
Popular deep learning models are trained on mass amounts of media scraped from the Internet, often utilizing copyrighted material. When assembling training data, the sourcing of copyrighted works may infringe on the copyright holder's exclusive right to control reproduction, unless covered by exceptions in relevant copyright laws. Additionally, using a model's outputs might violate copyright, and the model creator could be accused of vicarious liability and held responsible for that copyright infringement.
Since most legal jurisdictions only grant copyright to original works of authorship by human authors, the definition of "originality" is central to the copyright status of AI-generated works.
In the U.S., the Copyright Act protects "original works of authorship". The U.S. Copyright Office has interpreted this as being limited to works "created by a human being", declining to grant copyright to works generated without human intervention. Some legal professionals have suggested that Naruto v. Slater (2018), in which the U.S. 9th Circuit Court of Appeals held that non-humans cannot be copyright holders of artistic works, could be a potential precedent in copyright litigation over works created by generative AI. Some have suggested that certain AI generations might be copyrightable in the U.S. and similar jurisdictions if it can be shown that the human who ran the AI program exercised sufficient originality in selecting the inputs to the AI or editing the AI's output.
Proponents of this view suggest that an AI model may be viewed as merely a tool (akin to a pen or a camera) used by its human operator to express their creative vision. For example, proponents argue that if the standard of originality can be satisfied by an artist clicking the shutter button on a camera, then perhaps artists using generative AI should get similar deference, especially if they go through multiple rounds of revision to refine their prompts to the AI. Other proponents argue that the Copyright Office is not taking a technology neutral approach to the use of AI or algorithmic tools. For other creative expressions (music, photography, writing) the test is effectively whether there is de minimis, or limited human creativity. For works using AI tools, the Copyright Office has made the test a different one i.e. whether there is no more than de minimis technological involvement.
This difference in approach can be seen in the recent decision in respect of a registration claim by Jason Matthew Allen for his work Théâtre D'opéra Spatial created using Midjourney and an upscaling tool. The Copyright Office stated:
As AI is increasingly used to generate literature, music, and other forms of art, the U.S. Copyright Office has released new guidance emphasizing whether works, including materials generated by artificial intelligence, exhibit a 'mechanical reproduction' nature or are the 'manifestation of the author's own creative conception'. The U.S. Copyright Office published a Rule in March 2023 on a range of issues related to the use of AI, where they stated:
The Copyright Office further clarified in a January 2025 that AI-assisted works which the creative expression of the human remains evident in the work can be copyrighted, which can include creative adaption of prompts for AI generators or usage of AI to assist in creation process of a work such as filmmaking. Works "where the expressive elements are determine by a machine" still remain uncopyrightable. Both the federal and circuit courts in the District of Columbia have upheld the Copyright Office's refusal to register copyrights for works generated solely by machines, establishing that machine ownership would conflict with heritable property rights as establish by the Copyright Act of 1975.
The U.S. Patent and Trademark Office (USPTO) similarly codified restrictions on the patentability of patents credits solely to AI authors in February 2024, following an August 2023 ruling in the case Thaler v. Perlmutter. In this case, the Patent Office denied grant to patents created by Stephen Thaler's AI program, DABUS due to the lack of a "natural person" on the patents' authorship. The U.S. Court of Appeals for the Federal Circuit upheld this decision. In the subsequent rule-making, the USPTO allows for human inventors to incorporate the output of artificial intelligence, as long as this method is appropriately documented in the patent application. However, it may become virtually impossible as when the inner workings and the use of AI in inventive transactions are not adequately understood or are largely unknown.
Representative Adam Schiff proposed the Generative AI Copyright Disclosure Act in April 2024. If passed, the bill would require AI companies to submit copyrighted works to the Register of Copyrights before releasing new generative AI systems. These companies would have to file these documents 30 days before publicly showing their AI tools.
Other jurisdictions include explicit statutory language related to computer-generated works, including the United Kingdom's Copyright, Designs and Patents Act 1988, which states:
However, the computer generated work law under UK law relates to autonomous creations by computer programs. Individuals using AI tools will usually be the authors of the works assuming they meet the minimum requirements for copyright work. The language used for computer generated work relates, in respect of AI, to the ability of the human programmers to have copyright in the autonomous productions of the AI tools (i.e. where there is no direct human input):
The UK government has consulted on the use of generative tools and AI in respect of intellectual property leading to a proposed specialist Code of Practice: "to provide guidance to support AI firms to access copyrighted work as an input to their models, whilst ensuring there are protections on generated output to support right holders of copyrighted work". The U.S. Copyright Office recently published a notice of inquiry and request for comments following its 2023 Registration Guidance.
On November 27, 2023, the Beijing Internet Court issued a decision recognizing copyright in AI-generated images in a litigation.
As noted by a lawyer and AI art creator, the challenge for intellectual property regulators, legislators and the courts is how to protect human creativity in a technologically neutral fashion whilst considering the risks of automated AI factories. AI tools have the ability to autonomously create a range of material that is potentially subject to copyright (music, blogs, poetry, images, and technical papers) or other intellectual property rights (patents and design rights).
Deep learning models source large data sets from the Internet such as publicly available images and the text of web pages. The text and images are then converted into numeric formats the AI can analyze. A deep learning model identifies patterns linking the encoded text and image data and learns which text concepts correspond to elements in images. Through repetitive testing, the model refines its accuracy by matching images to text descriptions. The trained model undergoes validation to evaluate its skill in generating or manipulating new images using only the text prompts provided after the training process. When assembling these training datasets involves making copies of copyrighted works, this has raised the question of whether this process infringes the copyright holder's exclusive right to make reproductions of their works, or if it falls use fair use allowances.
U.S. machine learning developers have traditionally believed this to be allowable under fair use because using copyrighted work is transformative, and limited. The situation has been compared to Google Books's scanning of copyrighted books in Authors Guild, Inc. v. Google, Inc., which was ultimately found to be fair use, because the scanned content was not made publicly available, and the use was non-expressive.
Timothy B. Lee, in Ars Technica, argues that if the plaintiffs succeed, this may shift the balance of power in favour of large corporations such as Google, Microsoft, and Meta which can afford to license large amounts of training data from copyright holders and leverage their proprietary datasets of user-generated data. IP scholars Bryan Casey and Mark Lemley argue in the Texas Law Review that datasets are so large that "there is no plausible option simply to license all [of the data...]. So allowing [any generative training] copyright claim is tantamount to saying, not that copyright owners will get paid, but that the use won't be permitted at all." Other scholars disagree; some predict a similar outcome to the U.S. music licensing procedures.
One of the earliest case to challenge the nature of fair use for training AI was a lawsuit that Thomson Reuters brought against Ross Intelligence first filed in 2020. Thomson Reuters argued that Ross Intelligence had used their Westlaw headnotes, brief summaries of court decisions, to train their AI engine designed to compete with Westlaw. While Thomson Reuters' claims were initially denied by judge Stephanos Bibas of the Third Circuit on the basis that headnotes may not have been copyrightable, Bibas reevaluated his decision in February 2025 and issued a ruling favoring Thomson Reuters, in that headnotes are copyrightable, and that Ross Intelligence, which had since closed down in 2021, had inappropriately used the material. In the case of Ross's AI, the engine was not generative, and produced output that was composed of pieces of Westlaw's material, which aided in Thomson Reuter's claims of reuse, so how the case may apply to other generative AI like OpenAI is not clear.
In the EU, such TDM exceptions form part of the 2019 Directive on Copyright in the Digital Single Market. They are specifically referred to in the EU's AI Act (which came into force in 2024), which "is widely seen as a clear indication of the EU legislator’s intention that the exception covers AI data collection", a view that was also endorsed in a 2024 German court decision. Unlike the TDM exception for scientific research, the more general exception covering commercial AI only applies if the copyright holder has not opted out. As of June 2023, a clause in the draft AI Act required generative AI to "make available summaries of the copyrighted material that was used to train their systems".
Unlike the EU, the United Kingdom prohibits data mining for commercial purposes but has proposed this should be changed to support the development of AI: "For text and data mining, we plan to introduce a new copyright and database exception which allows TDM for any purpose. Rights holders will still have safeguards to protect their content, including a requirement for lawful access."
Indian copyright law provides fair use exceptions for scientific research, but lacks specific provisions for commercial AI training models. Unlike the EU and UK, India has not established text and data mining (TDM) provisions that explicitly address commercial AI systems. This regulatory uncertainty became apparent in 2024 when Asian News International (ANI) sued OpenAI for using its content to train AI models without authorization. While OpenAI offered an opt-out policy that ANI used in October 2024 to block AI scrapers, ANI claimed this measure was ineffective since their content remained available through content syndication. The case also highlighted jurisdictional challenges, as OpenAI argued it was not subject to Indian law because its servers and training operations were located outside the country.
In some cases, deep learning models may replicate items in their training set when generating output. This behaviour is generally considered an undesired overfitting of a model by AI developers, and has in previous generations of AI been considered a manageable problem. Memorization is the emergent phenomenon of LLMs to repeat long strings of training data, and it is no longer related to overfitting. Evaluations of controlled LLM output measure the amount memorized from training data (focused on GPT-2-series models) as variously over 1% for exact duplicates or up to about 7%. This is potentially a security risk and a copyright risk, for both users and providers. As of August 2023, major consumer LLMs have attempted to mitigate these problems, but researchers have still been able to prompt leakage of copyrighted material.
Under U.S. law, to prove that an AI output infringes a copyright, a plaintiff must show the copyrighted work was "actually copied", meaning that the AI generates output which is "substantially similar" to their work, and that the AI had access to their work.
In the course of learning to statistically model the data on which they are trained, deep generative AI models may learn to imitate the distinct style of particular authors in the training set. Since fictional characters enjoy some copyright protection in the U.S. and other jurisdictions, an AI may also produce infringing content in the form of novel works which incorporate fictional characters.
A generative image model such as Stable Diffusion is able to model the stylistic characteristics of an artist like Pablo Picasso (including his particular brush strokes, use of colour, perspective, and so on), and a user can engineer a prompt such as "an astronaut riding a horse, by Picasso" to cause the model to generate a novel image applying the artist's style to an arbitrary subject. However, an artist's overall style is generally not subject to copyright protection. Additional questions related to the copyrightability of style and the output of AI models was raised in March 2025, following an update to ChatGPT's model that was able to produce images strongly resembling the work of Studio Ghibli's artist Hayao Miyazaki. While users initially used it to make "Ghiblification" of popular meme images, further users were found to be distasteful in light of Miyazaki's negative stance on AI, and ChatGPT placed limits on the ability for users to make images in the style of living artists.