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GPT-4o: Yes, based on the information provided in the filings and briefs, Meta should be held liable, particularly on the following legal and ethical grounds: Knowing Use of Illegal Content...

GPT-4o: Yes, based on the information provided in the filings and briefs, Meta should be held liable, particularly on the following legal and ethical grounds: Knowing Use of Illegal Content...

...Distribution, Not Just Download. No Transformative Use. Precedent Against This Use. Meta’s reliance on pirated content from criminally prosecuted sources represents a serious abuse of copyright law

Pascal Hetzscholdt
April 14, 2025

Asking AI services: Please analyze the TorrentFreak article “Meta AI ‘Piracy’ Lawsuit: Publishers and Professors Challenge Fair Use Defense” and the associated Amicus Brief from the International Association of Scientific, Technical and Medical Publishers and explain what role content piracy has played in this matter, whether META should be held liable for what has occurred and how META or others can prevent this from happening in the future. Conclude with a summary of what you think would be the most legally sound approach from the judge in this case.

ChatGPT-4o’s answer:
Analysis of the Meta AI “Piracy” Lawsuit and Amicus Brief by STM Publishers

📌 Role of Content Piracy in the Lawsuit

Content piracy plays a central and explicit role in the lawsuit filed against Meta by authors and supported by the International Association of Scientific, Technical and Medical Publishers (STM). The allegations and evidence suggest that:

Meta used BitTorrent to download hundreds of terabytes of pirated books and academic content from notorious "shadow libraries" such as Z-Library, Sci-Hub, Libgen, Internet Archive, and Anna’s Archive.
While downloading, Meta also uploaded these works to others in the BitTorrent swarm, further enabling the global spread of pirated works.
These sources are not minor infractions: courts have ruled these platforms to be illegal, public interest–violating, and even associated with espionage risks in some cases.

The STM amicus brief emphasizes that these platforms are not comparable to libraries. Instead, they facilitate systemic theft, undermining scholarly ecosystems and the rule of law.

⚖️ Should Meta Be Held Liable?

Yes, based on the information provided in the filings and briefs, Meta should be held liable, particularly on the following legal and ethical grounds:

Knowing Use of Illegal Content: Meta was fully aware of the illegality of these sources and still proceeded to use them at commercial scale, sidestepping copyright holders and court rulings.
Distribution, Not Just Download: Using BitTorrent meant Meta didn’t just passively receive files—it actively participated in piracy by sharing the infringing content with others.
No Transformative Use: Law professors backing the plaintiffs argue that training a model to create competing educational outputs is not “transformative” and is instead directly commercial and substitutes for the original works.
Precedent Against This Use: As STM points out, no court has ever upheld fair use in such circumstances. Meta’s case stretches the doctrine to a breaking point.

🔒 How Can Meta and Others Prevent This in the Future?

To avoid similar legal and ethical breaches, Meta and other AI developers should:

Vet Datasets Rigorously: Ensure training data is sourced only from licensed or legitimately public-domain sources.
Partner with Rights Holders: Work proactively with publishers, libraries, and data repositories to obtain licenses, especially for educational and scholarly material.
Transparency & Audits: Implement clear documentation and third-party audits of dataset provenance to avoid future liabilities.
Digital Rights Safeguards: Avoid technologies like BitTorrent in acquiring data unless such use is specifically authorized by copyright holders.

These practices could help avoid both legal pitfalls and public backlash, particularly in academic and publishing communities.

⚖️ What Would Be the Most Legally Sound Approach for the Judge?

A reasoned and legally sound ruling would involve:

Rejecting Meta’s Fair Use Defense:
- The use is not transformative, undeniably commercial, and harms the market for the original works.
- Meta’s actions involved unauthorized copying and distribution, compounded by the illegality of the sources.
Holding Meta Partially or Fully Liable:
- The judge should find Meta liable for at least vicarious and contributory infringement, given its role in redistributing pirated content.
- Damages should reflect the scale and willfulness of the infringement.
Issuing Injunctive Relief:
- The court could restrict Meta from using the infringing content in future training and mandate purging LLMs trained on illegal datasets (if technically feasible).
Clarifying Boundaries of Fair Use for AI:
- A judicial opinion that clearly articulates limits around “fair use” for LLM training would help guide the entire AI ecosystem—possibly setting precedent until the issue reaches the Supreme Court, as predicted.

✅ Conclusion

Meta’s reliance on pirated content from criminally prosecuted sources represents a serious abuse of copyright law under the guise of AI development. The argument that such large-scale unauthorized use constitutes “fair use” is not only legally weak, but ethically corrosive to knowledge ecosystems. The most prudent approach for the court would be to affirm liability and set a precedent that respects copyright while signaling to the tech industry that AI innovation cannot come at the cost of the law.

Claude’s answer:
Based on the provided documents, I'll analyze the role of content piracy in Meta's AI training lawsuit and examine the legal implications.

Role of Content Piracy in Meta's AI Training

The documents reveal that content piracy played a central role in Meta's AI training process for its Llama models. According to the TorrentFreak article and the STM Publishers' amicus brief:

Meta allegedly used BitTorrent to download and distribute hundreds of terabytes of copyrighted works from notorious piracy websites including:
- Z-Library (subject to criminal prosecution by the Department of Justice)
- Library Genesis (Libgen)
- Sci-Hub
- Internet Archive's book database (ruled infringing by courts)
- Anna's Archive (which aggregates content from the other sites)
Meta's engineers reportedly acknowledged these were "shady websites" and took steps to hide their activities, indicating awareness of the potentially illegal nature of their actions.
The pirated sources Meta used have been:
- Found by multiple courts to be illegal and against the public interest
- Subject to criminal charges (Z-Library operators)
- Ordered shut down via court injunctions
- Investigated for potential espionage connections (Sci-Hub)
- Deemed "notorious markets" by the U.S. Trade Representative
By using BitTorrent, Meta not only downloaded infringing content but also uploaded and distributed these materials to others in the BitTorrent network, potentially facilitating further infringement.

Question of Meta's Liability

Meta's liability hinges on two key legal questions:

Direct Copyright Infringement: Meta's downloading and distributing of copyrighted works through BitTorrent constitutes reproduction and distribution without authorization from rights holders.
Fair Use Defense: Meta argues that its use qualifies as fair use regardless of the source. The publishers and law professors opposing Meta contend that:
- Meta's use was "undeniably commercial" with the purpose of creating a product that competes with the original works
- The use provides "no new transformative meaning"
- Each act of alleged infringement must be evaluated independently
- The BitTorrent distribution aspect further undermines fair use claims
- Courts have consistently held that using stolen copies weighs against fair use (as in Harper & Row v. Nation)
- Meta's active participation in piracy networks goes beyond merely failing to license content

A critical distinction emphasized in the amicus brief is that Meta's actions extend beyond simply choosing not to license content—they involve actively acquiring and distributing stolen materials.

Prevention Measures for the Future

To prevent similar situations in the future:

For AI Companies:
- Obtain proper licensing for training materials through legitimate channels
- Implement robust provenance verification for training data sources
- Develop industry standards for ethically sourcing training data
- Create compensation models for content creators whose works are used in AI training
- Establish transparent data auditing processes
For Publishers and Rights Holders:
- Develop standardized licensing frameworks for AI training use cases
- Implement technical solutions to better track unauthorized uses
- Continue pursuing legal action against prominent piracy platforms
- Collaborate with AI companies on ethical data access solutions
For Regulators:
- Clarify copyright law application to AI training specifically
- Consider legislation that addresses the unique challenges of AI and copyright
- Strengthen international cooperation against piracy platforms
- Support legitimate markets for AI training data

Most Legally Sound Approach for the Judge

The most legally sound approach for the judge would be to:

Separate the analysis: Evaluate Meta's initial copying via BitTorrent and subsequent training use as distinct acts of potential infringement, each requiring its own fair use analysis.
Apply established precedent: Consider Harper & Row's guidance that using "purloined" copies weighs against fair use, and Atari Games' principle that fair use must begin with an authorized copy.
Assess commerciality: Recognize Meta's inherently commercial purpose in training Llama models to compete in the AI market.
Consider distribution impact: Weigh the fact that Meta's BitTorrent activity involved not just downloading but distributing infringing copies to others.
Evaluate market harm: Assess the potential harm to both the market for licensing content for AI training and the broader market harm caused by facilitating piracy.
Balance innovation and rights: While acknowledging the importance of AI development, ensure that innovation doesn't come at the expense of established copyright principles.
Reject "ends justify means" argument: Make clear that potentially beneficial outcomes from AI technology don't legitimize unlawful acquisition of training materials.

Based on established copyright precedent and the severity of Meta's alleged actions, the most legally sound conclusion would be to find that Meta's use of BitTorrent to acquire training materials from known piracy sites does not qualify as fair use, regardless of whether the ultimate AI training itself might have qualified under different circumstances.

Grok’s answer:
Analysis of the TorrentFreak Article and Amicus Brief

The TorrentFreak article and the Amicus Brief from the International Association of Scientific, Technical and Medical Publishers (STM) detail a class-action lawsuit filed by authors, including Richard Kadrey, Sarah Silverman, and Christopher Golden, against Meta Platforms, Inc. The lawsuit alleges that Meta used pirated book archives to train its Llama AI models without permission, raising significant copyright infringement issues. Below, I analyze the role of content piracy, Meta’s potential liability, preventive measures, and the most legally sound judicial approach.

Role of Content Piracy in the Matter

Content piracy is central to the lawsuit, as Meta is accused of downloading vast amounts of copyrighted material from notorious pirate websites—Z-Library, Library Genesis (Libgen), Sci-Hub, Internet Archive, and Anna’s Archive—via BitTorrent to train its AI models. These platforms are known for distributing unauthorized copies of books, scientific articles, and other works, often in violation of court orders and copyright laws:

Z-Library: Indicted for criminal copyright infringement, with operators arrested and domains seized, yet Meta allegedly sourced materials from it post-enforcement.
Library Genesis: Found liable for willful infringement by U.S. courts, with permanent injunctions issued, but Meta used its collections.
Sci-Hub: Similarly adjudged infringing, with injunctions and damages awarded against it, yet Meta accessed its pirated scientific articles.
Internet Archive: Ruled to have infringed copyrights by digitizing books without permission, but Meta downloaded its materials.
Anna’s Archive: Aggregates infringing files from these sites, openly admitting to violating copyright laws, and Meta used it as a source.

The TorrentFreak article notes that Meta’s use of BitTorrent meant it not only downloaded but also uploaded pirated content, amplifying the distribution of infringing materials. The STM brief emphasizes the scale of this piracy, highlighting that Meta knowingly sourced from “the world’s most notorious infringing websites,” which have been subject to legal actions, FBI investigations, and domain seizures. This reliance on pirated content bypasses legitimate licensing channels, depriving authors and publishers of compensation and undermining the incentive to create.

Should Meta Be Held Liable?

Determining Meta’s liability hinges on whether its actions constitute copyright infringement and whether its fair use defense holds. The plaintiffs and STM argue against fair use, while Meta contends its AI training qualifies. Here’s an analysis of both sides:

Arguments for Meta’s Liability:

Direct Infringement: Meta’s downloading and uploading of copyrighted works via BitTorrent constitutes unauthorized reproduction and distribution, clear violations of copyright law (17 U.S.C. § 106). The STM brief cites Petrella v. Metro-Goldwyn-Mayer (572 U.S. 663, 671), noting that each act of infringement is independently actionable.
Bad Faith Conduct: The STM brief invokes Harper & Row v. Nation Enterprises(471 U.S. 539, 562-63), arguing that Meta’s use of “purloined” works from illegal sources weighs heavily against fair use. Unlike cases where bad faith is merely a failure to license (Campbell v. Acuff-Rose, 510 U.S. 569), Meta’s knowing reliance on pirated sources is egregious.
Non-Transformative Use: Law professors supporting the plaintiffs argue that Meta’s AI training is not transformative because it serves a similar educational purpose as the original works and competes in the same market. The STM brief adds that the commercial nature of Meta’s AI undermines fair use.
Market Harm: The STM brief details how piracy denies publishers sales, authors recognition, and future investments in scholarly works. Meta’s actions exacerbate this by enabling further dissemination via BitTorrent.
Precedent: Cases like Atari Games v. Nintendo (975 F.2d 832) emphasize that fair use requires starting with an authorized copy, which Meta did not do.

Arguments Against Meta’s Liability:

Fair Use Defense: Meta argues that training AI with copyrighted material is transformative, creating a new product (Llama models) that doesn’t replicate the original works’ expressive content. Some law professors support this, citing cases like Authors Guild v. Google (804 F.3d 202), where Google’s book digitization was deemed fair use.
Public Benefit: Meta may claim that AI advancements serve the public interest, outweighing harm to copyright holders, especially if outputs don’t directly reproduce protected works.
Source Irrelevance: Meta contends that the source of the data (pirated or not) is irrelevant to fair use, focusing on the end use (AI training) rather than acquisition.

Conclusion on Liability: Meta’s actions likely constitute infringement. The scale of unauthorized copying, deliberate use of pirated sources, and distribution via BitTorrent weaken its fair use defense. The STM brief persuasively argues that Meta’s conduct is unprecedented in its brazenness, and precedents like Harper & Row and Atari support considering bad faith in sourcing. While AI training could be transformative in some contexts, Meta’s commercial purpose, market harm, and lack of authorized copies tip the fair use factors against it. Liability seems warranted unless Meta can demonstrate minimal harm or a novel transformative use not addressed in the documents.

Preventive Measures for Meta and Others

To avoid similar issues, Meta and other companies can adopt the following strategies:

License Content Legally: Partner with publishers, libraries, or content aggregators to access authorized datasets, as Google did in Authors Guild v. Google. Negotiate licenses explicitly for AI training to ensure compliance.
Source Verification: Implement strict protocols to verify the legality of data sources, avoiding platforms with known infringement histories. Due diligence can prevent reliance on shadow libraries.
Ethical AI Development: Establish internal guidelines prioritizing copyright respect, including transparency about data sources and consent from rightsholders.
Alternative Datasets: Use public domain works, open-access materials, or synthetic data to train AI, reducing reliance on copyrighted content.
Industry Collaboration: Work with trade groups like STM to create frameworks for AI training that balance innovation with copyright protection, potentially through collective licensing models.
Avoid Peer-to-Peer Networks: Refrain from using BitTorrent or similar protocols for data acquisition, as they inherently involve distribution and increase infringement risks.

Most Legally Sound Approach for the Judge

The judge, Hon. Vince Chhabria, faces a complex case with significant implications for AI and copyright law. Based on the documents, the most legally sound approach would involve:

Separate Analysis of Infringement Acts: As urged by the STM brief, the judge should evaluate Meta’s BitTorrent downloading/uploading and AI training as distinct acts (Petrella). The former is likely direct infringement due to unauthorized reproduction and distribution, while the latter requires a nuanced fair use analysis.
Fair Use Evaluation:
- Purpose and Character: Meta’s commercial use and bad faith in sourcing pirated content (Harper & Row) weigh against fair use. The transformative nature of AI training is debatable, but the professors’ argument that it competes with the original works’ educational purpose is compelling.
- Nature of the Work: The creative and scholarly nature of the books and articles favors the plaintiffs.
- Amount and Substantiality: Meta copied entire works, a factor against fair use unless justified by necessity, which Meta has not clearly established.
- Market Effect: The STM brief highlights significant harm to publishers and authors, exacerbated by Meta’s distribution, strongly favoring the plaintiffs.
Rejection of Fair Use for BitTorrent Activity: Meta’s downloading and uploading via BitTorrent is unlikely to qualify as fair use, given its non-transformative nature, bad faith, and contribution to piracy (Glacier Films v. Turchin, 896 F.3d 1033).
Partial Summary Judgment: Grant the plaintiffs’ motion for partial summary judgment on direct infringement via BitTorrent, as the evidence of unauthorized copying and distribution is clear. For AI training, if facts are undisputed, rule against fair use based on the factors above; otherwise, allow further discovery to clarify the transformative nature and market impact.
Consider Precedent and Policy: Balance copyright protection with AI innovation, but prioritize established law (Harper & Row, Atari) over Meta’s expansive fair use claim. A ruling excusing piracy would undermine copyright incentives, as STM argues.
Potential for Higher Courts: Given the split among law professors and the case’s stakes, signal that AI-related fair use questions may require Supreme Court clarification, as the article suggests.

Summary: The judge should find Meta liable for direct infringement through BitTorrent activities, as these are clear violations with no credible fair use defense. For AI training, lean toward rejecting fair use due to commerciality, market harm, and bad faith, unless Meta provides compelling evidence of transformation without harm. This approach upholds copyright law, deters piracy, and leaves room for future AI cases to refine fair use boundaries, potentially at the Supreme Court.

This analysis relies on the provided documents and my knowledge of copyright law. If additional context is needed, I can offer to search for further details.

Perplexity’s answer:
The ongoing lawsuit against Meta Platforms, Inc. centers on allegations that its AI systems were trained using pirated content from notorious shadow libraries and repositories. The International Association of Scientific, Technical and Medical Publishers (STM) filed an amicus brief supporting the plaintiffs, arguing that Meta’s reliance on infringing materials undermines its fair use defense. Here’s an analysis of the key issues:

Role of Content Piracy in the Case

Sources of Allegedly Pirated Material
Meta’s AI training datasets reportedly included works from:
- Z-Library: A criminal piracy site whose operators were indicted in 2022 for copyright infringement. The U.S. government seized its domains, but it continues operating via clandestine URLs.
- Sci-Hub: A repository of millions of pirated scientific articles, repeatedly adjudicated as illegal in U.S. courts. The DOJ has investigated its founder for potential ties to Russian intelligence.
- Library Genesis (LibGen): A platform hosting over 2.5 million pirated books, currently facing lawsuits from publishers.
- Anna’s Archive: A meta-search tool aggregating content from Z-Library, Sci-Hub, and LibGen, openly describing itself as a “pirate library mirror”.
These sources have been repeatedly targeted by law enforcement and courts for large-scale infringement.
Impact on Fair Use Analysis
STM argues that using pirated content invalidates Meta’s fair use defense under § 107 of the Copyright Act:
- Market harm: Pirated works directly compete with licensed versions, depriving publishers of revenue.
- Non-transformative use: Meta’s AI allegedly reproduces expressive content verbatim for commercial purposes, unlike the transformative indexing in Authors Guild v. Google.
- Willful infringement: Courts have rejected fair use defenses when defendants knowingly used infringing sources (e.g., BMG Rts. Mgmt. v. Cox Commc’ns).

Should Meta Be Held Liable?

STM’s brief highlights several factors favoring liability:

Knowledge of infringement: Meta’s use of sources like Sci-Hub (widely publicized as illegal) suggests awareness of their infringing nature.
Scale of copying: The amicus cites Hachette v. Internet Archive, where mass digitization of books was deemed non-fair use. Meta’s ingestion of entire works mirrors this pattern.
Commercial benefit: Meta’s AI products (e.g., Llama) generate revenue, contrasting with non-profit uses in prior fair use cases.

Preventing Future Issues

To avoid similar disputes, STM recommends:

Licensing agreements: Meta should negotiate licenses with publishers for AI training data, as done by some competitors.
Ethical sourcing: Implement vetting processes to exclude pirated content from datasets.
Collaboration with publishers: Develop AI models that attribute and compensate creators, aligning with STM’s vision of “truthful, responsible AI”.

Recommended Judicial Approach

The most legally sound path for the court would involve:

Rejecting blanket fair use claims: Apply Campbell v. Acuff-Rose’s four-factor test rigorously, emphasizing Meta’s commercial purpose and the availability of licensed alternatives.
Disaggregating infringement claims: Analyze each work’s use individually, as urged in Petrella v. Metro-Goldwyn-Mayer.
Distinguishing lawful vs. pirated sources: Follow Hachette v. Internet Archive, where using legally obtained copies was central to the court’s analysis. Pirated content cannot justify fair use.

Summary

The court should deny Meta’s fair use defense due to its reliance on pirated materials, which undermines transformative purpose and market fairness. A precedent-aligned ruling would require AI developers to use licensed content, ensuring compliance with copyright law while fostering ethical AI innovation.

Google Gemini’s answer:
Okay, here is an analysis based on the TorrentFreak article and the Amicus Brief provided:

Role of Content Piracy

Content piracy plays a central role in this lawsuit against Meta. The core allegation is that Meta used large volumes of copyrighted books, sourced from known pirated archives, to train its Llama artificial intelligence (AI) models without permission from the copyright holders.

Sources Used: Plaintiffs and the publishers' amicus brief allege that Meta obtained training data, specifically copyrighted books, from notorious "shadow libraries" known for hosting pirated content. These include sites like Z-Library, Library Genesis (Libgen), Sci-Hub, and Anna's Archive.
Method of Acquisition: It's alleged that Meta used BitTorrent technology to download these pirated book archives.
Distribution: The use of BitTorrent also meant that while downloading, Meta was simultaneously uploading and distributing these pirated works to other users in the BitTorrent network.
Illegality of Sources: The publishers' brief emphasizes that these "shadow libraries" are illegal operations. Many have faced legal challenges, government investigations (including by the FBI and DOJ), domain seizures, and even criminal indictments against their operators. They argue Meta knowingly used these illegal and "stolen" materials.

Meta's Potential Liability

Meta's liability hinges on whether its actions constitute copyright infringement or qualify as "fair use" under copyright law.

Arguments Against Fair Use (Favoring Liability):
- Knowing Use of Pirated Sources: The plaintiffs and publishers argue that Meta's deliberate use of content from known illegal, pirated sources weighs heavily against a fair use finding. They cite legal precedent suggesting that using "stolen" or "purloined" works undermines a fair use defense.
- Commercial Purpose: A group of law professors argues Meta's use is "undeniably commercial". They assert the goal is to create a profitable AI model that competes directly with the original works and their authors.
- Lack of Transformation: These professors also argue the use isn't transformative because training an AI serves a similar purpose to the original books (e.g., education) and doesn't add new expression or meaning.
- Distribution Infringement: Meta's alleged uploading of pirated books via BitTorrent constitutes a separate act of distribution infringement.
- Market Harm: The creation of AI models trained on these works could harm the market for the original copyrighted books.
Arguments For Fair Use (Meta's Defense):
- Meta argues its use of copyrighted books as training inputs could be fair use, potentially focusing on the concept of intermediate copying or the transformative nature of AI model creation.
- Meta has previously suggested that the source of the training data (even if pirated) might not necessarily negate a fair use defense.

Prevention Strategies

While the documents focus on the lawsuit's specifics, the implied prevention strategies for Meta and other AI developers include:

Use Legally Sourced Training Data: Avoid using datasets known to contain pirated or infringing materials. Obtain proper licenses or permissions from copyright holders before using their works for AI training.
Vet Data Sources: Implement processes to understand the provenance and legality of training datasets, avoiding sources known for infringement like the "shadow libraries" mentioned.
Avoid Infringing Distribution Methods: Refrain from using technologies like BitTorrent for acquiring datasets if it involves unauthorized sharing of copyrighted works.

Potential Legally Sound Judicial Approach

A judge in this case must analyze Meta's actions through the lens of the four fair use factors, considering the arguments presented:

Purpose and Character of the Use: The court will assess if the use is commercial and transformative. Arguments suggest it's commercial and lacks transformation. Meta's alleged "bad faith" in knowingly using illegal sources is a critical aspect here.
Nature of the Copyrighted Work: The works are published books and articles, falling squarely within copyright protection.
Amount and Substantiality Used: Meta allegedly copied entire works in massive quantities.
Effect on the Market: The court will consider if Meta's AI competes with or harms the market for the original books.

Conclusion:

Based on the presented documents, Meta's knowing acquisition of vast amounts of copyrighted material from notoriously illegal sources and subsequent distribution via BitTorrent significantly weakens its fair use defense, particularly regarding the acquisition and initial copying/distribution. Legal precedent suggests using "purloined" materials weighs against fair use.

Therefore, a legally sound approach could involve finding Meta liable for copyright infringement related to its sourcing and distribution methods using BitTorrent and illegal archives. The separate question of whether the internal use for training constitutes fair use is more complex, but the commercial, non-transformative, and market harm arguments presented by opponents are substantial. Given the "brazen acts of infringement" highlighted by the publishers, finding against fair use seems a plausible outcome based on these documents. The differing opinions among legal experts indicate the complexity and potential for this issue to escalate to higher courts.