Explore AI copyright infringement and training data disputes, including AI-generated works, copyright in training datasets, fair use, Indian copyright law, global litigation and emerging legal frameworks.

Introduction

The rapid development of generative artificial intelligence has created a fundamental challenge for copyright law.

Modern AI systems can generate text, images, music, software code and other forms of content after being trained on enormous datasets. Those datasets may contain copyrighted books, articles, photographs, artwork, music, films, software repositories and other protected works.

This has generated a difficult legal question:

Can an AI developer lawfully copy and process copyrighted works to train an artificial intelligence model without obtaining permission from copyright owners?

The question becomes even more complicated when an AI-generated output resembles, reproduces or incorporates protected material.

Consequently, AI copyright disputes generally arise at two different stages:

Training-stage disputes — whether copyrighted works can lawfully be copied, collected, stored and processed during AI training; and
Output-stage disputes — whether content generated by an AI system infringes copyright in an existing work.

These questions are legally distinct and should not be treated as one general “AI copyright” issue.

The debate involves competing interests. Copyright owners argue that AI companies should not be permitted to commercially exploit copyrighted works without authorisation. AI developers argue that large-scale computational analysis may involve technically necessary copying and that restricting training could substantially impair innovation.

Courts and legislators around the world are now attempting to reconcile these competing interests.

For India, the issue is particularly significant because the Copyright Act, 1957 was drafted long before generative AI existed. While Indian copyright law contains provisions concerning fair dealing, computer programmes, infringement and statutory licensing, it does not contain a comprehensive AI-training exception comparable to some emerging text-and-data-mining frameworks elsewhere.

This article examines the legal issues surrounding AI copyright infringement and training data disputes, compares major international approaches, examines important litigation and considers how Indian copyright law may respond to generative AI.

1. What Is AI Copyright Infringement?

AI copyright infringement can broadly refer to the unauthorised use of copyrighted material in connection with the development, training, operation or output of an AI system.

However, the expression encompasses several distinct legal situations.

1.1 Training-data infringement

An AI company may collect millions or billions of copyrighted works from:

websites;
books;
academic publications;
news archives;
image repositories;
social media;
software repositories;
music databases; and
other online sources.

The copyright question is whether the copying involved in collecting and processing those works requires authorisation.

1.2 Memorisation and reproduction

An AI model may sometimes reproduce portions of training material.

If an AI system generates a substantially similar or identical reproduction of protected content, the copyright owner may argue that the output infringes copyright.

1.3 Style imitation

Artists have also challenged systems capable of generating images that closely imitate distinctive artistic styles.

A major legal distinction arises here:

Copyright generally protects expression rather than an abstract artistic style.

Therefore, merely producing an image “in the style of” an artist does not necessarily constitute copyright infringement. The legal position becomes more complicated where the output reproduces protected expressive elements from a particular work.

1.4 AI-generated works

Another question is whether the AI-generated output itself receives copyright protection.

This is different from determining whether the output infringes somebody else’s copyright.

The two questions are:

Can the AI output be protected?

and

Does the AI output infringe an existing copyrighted work?

They should not be conflated.

2. Why Training Data Has Become the Central Copyright Dispute

Generative AI systems require enormous quantities of data.

Large language models may be trained using combinations of:

books;
websites;
articles;
public-domain materials;
licensed datasets;
user-generated content;
code;
academic papers; and
other textual material.

Image-generation models may similarly process:

photographs;
illustrations;
paintings;
graphic designs;
advertisements; and
other visual works.

The scale of this process distinguishes AI training from conventional human learning.

A human reading a copyrighted book does not normally create billions of digital copies of that book.

AI training, however, may involve technically copying and processing large quantities of works.

The legal question is therefore not simply whether the AI “learns” from copyrighted material.

The question is:

What acts of reproduction, storage, extraction, transformation and processing occur during the training process, and does copyright law permit those acts?

3. Copyright and the AI Training Pipeline

AI development can involve several stages:

Data collection → Data scraping → Storage → Cleaning → Pre-processing → Training → Fine-tuning → Testing → Deployment

For example, a company could potentially argue that:

copying a work for temporary computational analysis is different from publishing the work;
training does not necessarily substitute for the original work;
the model does not contain conventional copies of every training work; and
the purpose is computational analysis rather than consumption of the work.

copying still constitutes reproduction;
commercial AI training can exploit the economic value of protected works;
AI outputs may compete with original works;
creators did not consent to the use of their works; and
large-scale training can undermine existing licensing markets.

The legal dispute therefore turns heavily on the interpretation of the relevant copyright exception.

4. Fair Use and AI Training in the United States

The United States has become one of the most important jurisdictions for AI copyright litigation because of its flexible fair use doctrine.

Section 107 of the U.S. Copyright Act identifies four principal factors:

purpose and character of the use;
nature of the copyrighted work;
amount and substantiality of the portion used; and
effect upon the potential market for the copyrighted work.

The first and fourth factors have become particularly important in AI cases.

4.1 The transformative-use argument

AI developers may argue that training transforms copyrighted works into a computational system rather than merely republishing them.

For example, an AI model does not ordinarily present a user with the entire dataset from which it was trained.

Developers may therefore argue that the purpose of copying is different from the purpose for which the original work was created.

However, courts must determine whether the AI system’s use is genuinely transformative and whether the commercial nature of the AI service weighs against fair use.

5. The Market-Harm Question

Perhaps the most consequential issue is whether AI systems compete with copyright owners.

Suppose an AI system is trained on millions of articles and can subsequently produce articles answering questions that users might otherwise have paid journalists or publishers to produce.

A copyright owner could argue:

The AI system is not merely analysing the works; it is commercially exploiting them and competing in the same market.

AI developers may respond that copyright law does not grant authors a monopoly over every future technology capable of processing or learning from their works.

The conflict is therefore increasingly about copyright’s relationship with technological substitution.

6. The New York Times v. OpenAI and Microsoft

One of the most prominent AI copyright disputes is The New York Times Company v. Microsoft Corporation et al.

The litigation concerns allegations relating to the use of The New York Times’s copyrighted content in the development and operation of generative AI systems.

The case illustrates the central legal questions surrounding:

training data;
reproduction;
AI outputs;
market substitution;
memorisation;
licensing markets; and
technological innovation.

The litigation is particularly important because it involves a major news organisation arguing that AI systems can exploit the economic value of copyrighted journalism.

The case could influence how courts evaluate the relationship between AI training and the commercial market for copyrighted works.

7. Authors Guild and Other Copyright Litigation

Authors and publishers have also challenged the use of books and written works for AI training.

One of the most important earlier cases is Authors Guild v. Google, although it concerned Google’s book digitisation project rather than generative AI.

The U.S. Supreme Court declined to hear the Authors Guild’s challenge after lower courts concluded that Google’s digitisation and search functionality constituted fair use.

The case is relevant to AI because it demonstrates how courts may evaluate large-scale copying undertaken for a new technological purpose.

However, it would be incorrect to assume that the Google Books decision automatically establishes that AI training is fair use.

Generative AI introduces additional considerations, particularly concerning:

commercial substitution;
output generation;
model memorisation; and
competition with copyright owners.

8. Image-Generation Litigation

Visual artists have brought significant copyright claims against generative AI companies.

A notable dispute involved Andersen v. Stability AI Ltd., brought by artists against companies involved in AI image-generation systems.

The litigation raised questions concerning:

training image datasets;
copying of artworks;
AI-generated images;
derivative works;
artist identity;
trademarks; and
unfair competition.

These cases demonstrate that AI copyright disputes may extend beyond copyright infringement itself.

Artists may also rely upon other legal claims where the circumstances permit.

9. Getty Images v. Stability AI

Getty Images has also pursued litigation against Stability AI concerning the alleged use of Getty’s visual content in AI model development.

This dispute illustrates another major issue:

licensed content versus unlicensed training data.

Businesses increasingly monetise their archives through licensing arrangements.

If AI developers can obtain similar material through large-scale scraping without paying licensing fees, copyright owners may argue that AI creates an artificial substitute for the existing licensing market.

The outcome of such disputes may influence whether AI companies increasingly adopt licensed training datasets.

10. The UK Approach: Text and Data Mining

The United Kingdom has adopted a more specific framework concerning text and data mining.

The UK’s copyright legislation contains an exception for computational analysis of copyright works in certain circumstances.

The existing framework distinguishes between non-commercial research and broader commercial uses.

This illustrates an important policy option:

Instead of asking courts to stretch traditional fair-dealing principles, legislation can create specific rules for computational analysis.

However, the scope and adequacy of such exceptions remain subjects of ongoing policy debate as generative AI develops.

11. The European Union Approach

The European Union has taken a particularly significant legislative approach through the Digital Single Market Directive.

Articles 3 and 4 establish text-and-data-mining exceptions under specified conditions.

The distinction is important.

Article 3

Article 3 addresses text and data mining for certain scientific research purposes by research organisations and cultural heritage institutions, subject to statutory conditions.

Article 4

Article 4 establishes a broader text-and-data-mining exception, but rights holders can reserve their rights against such uses in the manner provided by the Directive.

This introduces an important concept for AI:

The opt-out mechanism.

Rights holders may, under specified conditions, reserve their rights against certain forms of text and data mining.

12. The EU AI Act and Copyright

The EU’s Artificial Intelligence Act adds another layer to the copyright debate.

Providers of general-purpose AI models must adopt policies intended to ensure compliance with EU copyright law.

They must also make publicly available a sufficiently detailed summary of the content used for training the general-purpose AI model.

This represents an important regulatory shift.

The focus is not simply:

“Was copyright infringed?”

It also includes:

“Can the AI developer demonstrate responsible and transparent data governance?”

The EU framework therefore connects AI governance with copyright compliance.

13. Copyright Transparency and Training Data

Transparency has become one of the most important emerging principles.

What datasets were used?
Which sources were included?
Were copyrighted works used?
Were licences obtained?
Were rights reservations respected?
Was synthetic data used?
How was the data processed?

AI companies may resist full disclosure because training datasets can contain commercially sensitive information and revealing the exact dataset could create security and competitive concerns.

The legal challenge is therefore to balance:

copyright transparency + trade secrets + cybersecurity + innovation.

14. The Indian Copyright Framework

India’s principal copyright statute is the Copyright Act, 1957.

The Act protects various categories of works, including:

literary works;
dramatic works;
musical works;
artistic works;
cinematograph films; and
sound recordings.

The central issue for AI training is whether copying protected works for computational analysis falls within an existing statutory exception.

15. Fair Dealing Under Indian Copyright Law

Section 52 of the Copyright Act contains important exceptions to copyright infringement.

Unlike the broad U.S. fair-use doctrine, Indian copyright law generally operates through specific statutory exceptions.

Section 52 includes exceptions relating to purposes such as:

private or personal use;
criticism or review;
reporting current events; and
specified educational and research purposes.

The exact applicability of these exceptions to commercial AI training remains legally uncertain.

This creates a significant difference between India and the United States.

The Indian framework does not simply provide courts with a broad four-factor fair-use test equivalent to Section 107 of the U.S. Copyright Act.

16. Can AI Training Qualify as Fair Dealing in India?

This is one of the most important unresolved questions.

A developer might argue that training involves:

research;
computational analysis;
non-expressive processing; and
technological transformation.

However, several difficulties arise.

First, many AI models are developed for highly commercial purposes.

Second, the statutory exceptions under Section 52 must be interpreted according to their specific language.

Third, large-scale copying of copyrighted works may exceed what would traditionally be understood as fair dealing.

Fourth, AI-generated outputs can potentially compete with the original works.

Therefore, whether AI training qualifies as fair dealing in India will likely depend heavily on the facts and judicial interpretation.

17. Copyright Ownership of AI-Generated Content

Training-data disputes should be distinguished from the question of copyright ownership in AI-generated content.

This raises the question:

Can an AI system itself be the author of a copyrighted work?

Most copyright systems do not currently treat AI as an independent copyright-owning legal person.

The more relevant question is whether sufficient human creativity and control exists in the process.

For example, a person who:

develops a detailed creative concept;
selects and arranges elements;
gives creative instructions;
repeatedly modifies outputs; and
exercises meaningful creative control

may have stronger arguments for copyright protection than a person who simply enters a minimal prompt and accepts the resulting output.

The precise threshold remains jurisdiction-specific.

18. AI Outputs That Reproduce Copyrighted Works

Training disputes become even more complicated when an AI model produces an output that closely resembles an existing copyrighted work.

Potential legal questions include:

Substantial similarity

Is the generated output substantially similar to protected expression?

Reproduction

Does the output reproduce a substantial part of the original?

Derivative work

Does the output constitute an adaptation or derivative work?

Access

Can the claimant establish that the AI system had access to the original work?

Independent generation

Can the developer show that the output was independently generated rather than reproduced from memorised material?

These questions may require technical evidence concerning how the model operates.

19. Memorisation: The Missing Link Between Training and Infringement

One of the most technically significant issues is memorisation.

AI models do not necessarily store training data as conventional searchable databases.

Nevertheless, research has demonstrated that some models can reproduce portions of training material, particularly where content is repeated or unusually represented in training data.

This creates a possible distinction:

Learning from a work ≠ reproducing the work.

A copyright claim becomes stronger where an AI system can be prompted to reproduce protected expression substantially similar to the original.

The legal significance of memorisation will likely become increasingly important as litigation becomes more technically sophisticated.

20. The Role of Licensing

One potential solution is the development of large-scale licensing markets for AI training data.

Possible models include:

Direct licensing

AI companies negotiate licences directly with publishers, artists or rights holders.

Collective licensing

Collecting societies or industry organisations negotiate licences on behalf of multiple copyright owners.

Dataset licensing

Specialised providers offer curated datasets specifically licensed for AI training.

Revenue-sharing models

AI developers pay rights holders based on usage, revenue or model deployment.

Licensing could reduce litigation risk but raises difficult questions about:

pricing;
attribution;
valuation;
collective bargaining;
small creators;
international rights; and
enforcement.

21. Opt-Out Mechanisms

Another possible framework is the opt-out model.

Under such a system:

AI developers can conduct text and data mining;
copyright owners can reserve their rights;
AI developers must respect technically valid reservations.

The EU’s text-and-data-mining framework provides an important example.

For AI companies, however, an opt-out regime is only effective if developers have the technical capability to:

identify reservations;
prevent protected works from entering training datasets; and
document compliance.

22. Opt-In Versus Opt-Out

The policy debate can be simplified into two models.

Model	Basic Principle	Advantage	Concern
Opt-in	AI training requires permission	Strong creator protection	High transaction costs
Opt-out	Training permitted unless rights reserved	Facilitates innovation	Creators may lose control
Licensing	Training based on negotiated licences	Predictable compensation	Complex and expensive
Statutory exception	Parliament creates defined exception	Legal certainty	Difficult to design correctly
Hybrid	Combines exceptions, licensing and opt-outs	Flexible	Regulatory complexity

There is no universally accepted solution.

The appropriate model depends upon the policy objectives of the jurisdiction.

23. The Economic Dimension of AI Copyright Disputes

It involves significant economic questions.

reduce demand for original works;
substitute for licensed content;
depress creative-market prices;
reproduce distinctive creative expression; and
undermine existing licensing models.

AI companies, on the other hand, argue that:

machine learning requires large datasets;
excessive licensing costs could concentrate AI development among a few corporations;
training can generate new technological capabilities;
computational analysis is different from ordinary content consumption; and
broad restrictions could impede research and innovation.

24. The “Style” Controversy

Artists frequently complain that AI systems imitate their distinctive styles.

This raises a difficult doctrinal question.

For example, the abstract idea of producing art in a particular visual style may not itself constitute protected expression.

However, a generated image that reproduces identifiable expressive elements of a specific copyrighted work can raise a different issue.

Therefore:

Style imitation ≠ automatic copyright infringement.

The analysis must focus on protected expression and the specific facts.

25. AI and Moral Rights

Depending upon the jurisdiction, creators may possess rights concerning:

attribution;
integrity of the work;
distortion or mutilation; and
protection against false association.

This may become particularly relevant where AI systems generate altered versions of an artist’s work or create material falsely suggesting that a creator endorsed AI-generated content.

India recognises certain moral rights through Section 57 of the Copyright Act.

26. The Importance of Evidence in AI Copyright Litigation

AI copyright cases will increasingly depend upon technical evidence.

Relevant evidence may include:

dataset records;
web-scraping logs;
model documentation;
training procedures;
licensing agreements;
system prompts;
output logs;
model evaluations;
memorisation tests;
filtering systems;
copyright-management policies; and
records of rights-holder opt-outs.

This creates a new category of legal expertise combining:

copyright law + technology + data science + digital forensics.

Lawyers handling AI copyright litigation will therefore increasingly need to understand the technical architecture of AI systems.

27. Burden of Proof and Information Asymmetry

This creates an asymmetry:

Creator: knows what work was copied.

AI developer: knows what data was actually used.

A fair legal system must address this imbalance without automatically presuming infringement.

Possible solutions include:

court-ordered disclosure;
confidentiality-protected dataset inspection;
expert examination;
audit mechanisms;
preservation obligations; and
carefully defined evidentiary presumptions.

The challenge is to provide meaningful access to evidence while protecting trade secrets.

28. Defences Available to AI Developers

Depending on jurisdiction and facts, AI developers may potentially rely upon several arguments.

28.1 Fair use or fair dealing

The developer may argue that training is legally permissible under an applicable copyright exception.

28.2 Lack of substantial similarity

The output may not reproduce protected expression.

28.3 Lack of copying

The developer may dispute whether the alleged protected material was actually copied or retained in a legally relevant manner.

28.4 Independent creation

The developer may argue that an output was not derived from the claimant’s work.

28.5 Public domain

The underlying material may no longer be protected by copyright.

28.6 Licence

The developer may have obtained contractual permission to use the material.

28.7 Functional use

In some contexts, copying may be necessary to perform a technological or functional process and may fall within an applicable exception.

The availability and strength of these defences depend entirely upon the jurisdiction and facts.

29. Potential Remedies for Copyright Owners

Where infringement is established, remedies can potentially include:

injunctions;
damages;
account of profits;
delivery-up or destruction of infringing copies;
statutory remedies where available;
licensing arrangements;
corrective measures; and
orders concerning future use of protected material.

For AI systems, courts may face an unusual remedial question:

What should happen to a trained model alleged to contain knowledge derived from infringing copies?

Destroying an entire model could have consequences far beyond conventional copyright remedies.

A court may therefore need to consider:

whether the model itself is infringing;
whether the problematic training material can be removed;
whether retraining is technically feasible;
whether filtering can prevent reproduction; and
whether monetary compensation is sufficient.

30. The Problem of Model Unlearning

One emerging technical concept is machine unlearning.

It refers broadly to methods intended to remove the influence of specified data from a trained model.

If courts determine that particular copyrighted works were unlawfully used, an important future question may be whether AI companies can be required to “unlearn” those works.

However, technical unlearning is not necessarily equivalent to simply deleting a file from a database.

The legal system will therefore need technical evidence to determine whether an alleged remedy is actually feasible.

31. India’s Need for Legislative Clarity

India faces an important policy choice.

It could rely primarily upon judicial interpretation of existing copyright exceptions.

Alternatively, Parliament could introduce a specific framework addressing AI training.

Possible legislative models include:

Model 1: Specific AI training exception

Permit defined forms of AI training subject to statutory safeguards.

Model 2: Licensing framework

Require commercial AI developers to obtain licences for specified categories of copyrighted works.

Model 3: Opt-out framework

Permit training subject to a technically enforceable rights-reservation mechanism.

Model 4: Hybrid framework

Combine:

research exceptions;
commercial licensing;
transparency;
opt-out mechanisms; and
remuneration requirements.

A hybrid model may ultimately provide the greatest flexibility.

32. Policy Recommendations for India

A balanced Indian framework could consider the following measures.

32.1 Define AI training expressly

Legislation should clarify whether “training” includes:

scraping;
temporary copying;
storage;
preprocessing;
model training;
fine-tuning; and
evaluation.

32.2 Introduce transparency requirements

AI developers using large-scale copyrighted datasets should maintain appropriate records.

32.3 Protect small creators

Any licensing mechanism should not favour only large publishers and corporations.

Independent artists, authors, photographers and developers should have practical mechanisms to exercise their rights.

32.4 Encourage licensed datasets

Government and industry standards could encourage the development of lawful AI-training datasets.

32.5 Protect innovation

Research institutions and smaller AI developers should not face regulatory requirements designed only for large commercial AI providers.

32.6 Establish clear remedies

The law should distinguish between:

unlawful training;
accidental memorisation;
deliberate reproduction; and
substantially infringing outputs.

Different violations may warrant different remedies.

33. Future of AI Copyright Litigation

AI copyright litigation is likely to expand beyond the initial question of whether training is lawful.

Future disputes may concern:

synthetic data;
model distillation;
fine-tuning;
retrieval-augmented generation;
AI agents;
multimodal models;
voice cloning;
digital replicas;
software code generation;
AI-generated music;
model outputs that reproduce protected content; and
contractual restrictions imposed by content platforms.

The legal boundary between learning, copying and transforming will become increasingly important.

34. Key Legal Questions Courts Will Need to Answer

Future courts may need to determine:

Is copying for AI training legally equivalent to ordinary reproduction?
Does AI training constitute fair use or fair dealing?
Does commercial AI development alter the analysis?
When does an AI output become substantially similar to a copyrighted work?
Can a model itself be considered an infringing reproduction?
How should courts treat memorised training data?
Who bears the burden of proving what training data was used?
Can copyright owners demand disclosure of training datasets?
Can rights holders effectively opt out?
Can an AI model be ordered to undergo technical “unlearning”?
Who owns copyright in AI-assisted works?
How should moral rights apply to AI-generated content?
How should copyright law interact with data-protection and personality rights?
Should commercial AI training require remuneration?
What rules should apply to cross-border AI development?

The answers to these questions will shape the future relationship between copyright and artificial intelligence.

35. Conclusion

AI copyright infringement and training-data disputes represent one of the most consequential legal debates created by generative artificial intelligence.

The central problem is not simply whether AI systems “copy” human creativity. The deeper question is whether copyright law—originally designed around books, paintings, recordings, films and software—can adequately regulate computational systems capable of analysing enormous quantities of creative works and generating new content at unprecedented scale.

The legal debate must distinguish between at least three separate questions:

First: Was copyrighted material lawfully used to train the AI system?

Second: Does the resulting AI model retain or reproduce protected expression?

Third: Does a particular AI-generated output infringe copyright?

Different legal rules may apply at each stage.

The United States is testing these questions through litigation under the flexible fair-use doctrine. The European Union has adopted a more structured approach combining text-and-data-mining rules, transparency obligations and AI regulation. India currently relies primarily on its existing copyright framework, particularly the statutory exceptions under the Copyright Act, 1957, leaving substantial uncertainty regarding commercial AI training.

The long-term solution is unlikely to be either “all AI training is infringement” or “all AI training is fair use.”

A more sustainable approach would balance:

innovation + creator rights + transparency + licensing + technological feasibility + public interest.

For India, the priority should be creating legal certainty without prematurely locking the country into a framework that could inhibit AI research and development.

The future of copyright law may ultimately depend upon a new principle:

AI should be permitted to learn from human creativity only within a legal framework that recognises both the economic value of that creativity and the public value of technological innovation.

The objective should therefore be neither unrestricted extraction of copyrighted works nor absolute control over computational learning. Instead, copyright law must develop a carefully calibrated framework in which innovation is encouraged, creators are treated fairly, and AI developers have clear, predictable rules for acquiring and using training data.

Frequently Asked Questions

What is AI copyright infringement?

AI copyright infringement occurs when an AI developer, provider, user or system uses, reproduces, distributes or generates protected copyright material in a manner that violates applicable copyright law.

Is AI training on copyrighted material illegal?

Not necessarily. The legality depends on the jurisdiction, purpose of the use, applicable copyright exceptions, licences, rights reservations and the manner in which the material is copied and processed.

Is AI training fair use in the United States?

There is no universal rule that AI training is either fair use or infringement. Courts must analyse the specific facts under the statutory fair-use framework.

Is AI training permitted under Indian copyright law?

Indian law does not currently contain a comprehensive AI-specific training exception. The applicability of existing Section 52 exceptions will depend upon the circumstances and judicial interpretation.

Can artists sue AI companies for using their artwork?

Potentially, depending upon the facts and applicable law. Claims may concern training-stage copying, outputs, reproduction, derivative works, contractual rights, trademarks or other legal protections.

Can AI-generated content be copyrighted?

Copyright protection for AI-generated material depends upon the applicable jurisdiction and the degree of human creative contribution involved.

What is the difference between AI training infringement and AI output infringement?

Training infringement concerns the copying and processing of copyrighted works during AI development. Output infringement concerns whether material generated by the AI system reproduces protected expression from an existing work.

What is the biggest legal challenge in AI copyright disputes?

One of the biggest challenges is determining what training data was actually used and how it was processed. AI developers often possess much more information about the training process than individual copyright owners.

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