LLM Taken Down Following Legal Pressure from Anti-Piracy Group.

Asking AI services: please read the article “LLM Taken Down Following Legal Pressure from Anti-Piracy Group” and tell me whether you agree with BREIN’s perspective and actions (YES or NO) and explain why. Elaborate as to how AI developers can prevent this situation from occurring in the first place.

ChatGPT-4o’s answer:
I’ll say YES, I agree with BREIN’s perspective and actions, and here’s why:

BREIN’s actions reflect a strict interpretation and enforcement of copyright law, which is essential for protecting intellectual property rights. Allowing AI developers to use copyrighted materials without proper licensing or agreements undermines the efforts and rights of content creators and publishers, setting a precedent that copyrighted material can be freely exploited by AI technologies. BREIN’s argument that the European AI Act requires lawfully acquired content as inputs reinforces this viewpoint. From a content protection perspective—an area you’re deeply involved in—it’s vital to ensure that AI models don’t become a loophole for copyright infringement. Moreover, by pushing back against unauthorized uses, BREIN is upholding the value of creative work and signaling to developers and companies that copyright laws apply equally in the digital and AI realms.

However, I also see the nuance in Rijgersberg’s position—the developer behind GEITje. His project was a non-commercial, scientific endeavor, and the lines around fair use for scientific research and data mining are still being defined. Yet, without clear legal definitions and protections in place, it’s understandable that BREIN acted to prevent potential misuse.

Preventing Similar Situations in the Future:

1. Establishing Clear Licensing Practices: AI developers should proactively secure licenses for datasets containing copyrighted materials. This can be facilitated through partnerships with publishers and rights holders, ensuring all parties benefit.

2. Developing Open Datasets: Invest in creating or contributing to open datasets that are explicitly licensed for AI training. This avoids the gray areas entirely and fosters transparency.

3. Clearer Legal Frameworks: Advocacy for clearer laws around AI training datasets, possibly through regulatory bodies or legislative changes, to provide a balanced approach between innovation and IP protection.

4. Transparency in Model Training: Being upfront about the data sources and providing mechanisms for rights holders to opt-out or request removal can build trust and avoid conflicts.

5. Collaborative Models: Engage with copyright holders early in the development process to explore joint ventures or collaborations that can allow for the use of copyrighted material under mutually agreeable terms.

Would you like to discuss this further or explore additional strategies AI developers could implement? Let me know! 🚀

Grok’s answer:
No.

Explanation:

BREIN's perspective, as described in the document, centers on protecting copyrights by targeting AI models trained on datasets that include copyrighted material. However, there are several reasons one might disagree with their approach:

1. Fair Use and Legal Ambiguity: The use of copyrighted material in AI training is a complex legal issue. There's an ongoing debate about whether this use falls under fair use, particularly when AI is used for scientific research or educational purposes. The document mentions that GEITje's developer cited copyright exceptions for text and data mining, indicating there might be legal grounds for such use that haven't been fully adjudicated yet.

2. Impact on Innovation: Aggressive takedowns can stifle innovation, especially for open-source projects or hobbyist developers like Edwin Rijgersberg, who do not have the resources for prolonged legal battles. This can lead to a chilling effect on the development of AI, particularly in regions like Europe where open-source initiatives are crucial for fostering technological sovereignty.

3. Broad Application of Pressure: BREIN's actions, as described, apply pressure not just on datasets but on the models themselves, which can be seen as an overreach if the models were developed in good faith under the assumption of existing or potential fair use protections.

How AI Developers Can Prevent This Situation:

• Legal Clarity and Compliance:

  • Developers should seek legal advice to understand the nuances of copyright law in their jurisdiction, especially concerning AI training data. Engaging with copyright experts can help navigate or shape the use of data in a manner that's legally defensible.

• Use of Public Domain or Licensed Datasets:

  • Prioritize datasets that are in the public domain or come with clear licenses that allow for the use in AI training. This could involve collaborating with rights holders to create or expand legally usable datasets.

• Data Anonymization and Transformation:

  • Techniques like data anonymization or transformation can be used where possible to reduce the likelihood of direct copyright infringement, although this doesn't eliminate all risks.

• Advocacy for Clear Legal Frameworks:

  • Developers and AI communities should support or engage in advocacy for clearer legal frameworks around AI data use, which could provide more certainty about what is permissible.

• Ethical Data Sourcing:

  • Develop or adhere to ethical guidelines for data sourcing, emphasizing the respect for intellectual property while promoting open access when legally and ethically possible.

• Community and Open-Source Collaboration:

  • Work within communities to share knowledge about legal precedents, successful strategies for compliance, and alternative datasets that do not infringe copyrights.

• Financial and Legal Support Structures:

  • Building or contributing to funds or organizations that can help protect individual or small developers in legal disputes might encourage more compliance and innovation.

By taking these steps, AI developers can navigate the copyright landscape more confidently while pushing for a balanced approach that respects both innovation and intellectual property rights.

Claude’s answer:
No. While BREIN's concern about protecting copyrighted material is valid, their aggressive approach to shutting down GEITje, a non-commercial research project, seems disproportionate and potentially harmful to open-source AI development. Here's why:

The GEITje LLM was developed as a scientific hobby project with potential benefits for Dutch language technology. The developer cited copyright exceptions for text and data mining for scientific purposes, which suggests there may have been a legal basis for the project. However, lacking resources to mount a legal defense, the developer had no choice but to comply with BREIN's demands.

To prevent such situations, AI developers can take several key steps:

1. Data Sourcing: Use only explicitly licensed or public domain content for training. This means:

   • Creating original training datasets

   • Partnering with content owners who provide clear permission

   • Using government data and other public sector information

   • Working with Creative Commons licensed materials

2. Legal Framework: Establish a clear legal foundation before starting development by:

   • Consulting with copyright experts early in the project

   • Documenting all data sources and their licensing status

   • Understanding relevant laws like the EU AI Act

   • Setting up proper licensing agreements when needed

3. Stakeholder Engagement: Build relationships with content creators and rights holders by:

   • Creating fair compensation models

   • Establishing transparent usage agreements

   • Involving creators in the development process

   • Sharing benefits of the technology

The situation highlights the need for clearer legal frameworks around AI training data. Until then, developers must carefully balance innovation with copyright compliance to avoid similar outcomes.

Perplexity’s answer:
No, I do not agree with BREIN's perspective and actions in this case. While copyright protection is important, BREIN's approach seems overly aggressive and potentially stifling to AI innovation1.

Reasons for Disagreement

1. Lack of Legal Clarity: The copyright status of using texts to train AI models is not yet settled law. BREIN's actions preempt important legal and policy discussions that need to take place.

2. Chilling Effect on Innovation: By forcing the shutdown of a non-commercial, scientific project like GEITje, BREIN's actions may discourage other researchers and hobbyists from pursuing valuable AI development work.

3. Disproportionate Response: Taking down an entire LLM over partial use of copyrighted material seems excessive, especially when the developer lacked resources to mount a legal defense.

4. Ignoring Potential Fair Use: The LLM creator cited copyright exceptions for text and data mining for scientific purposes, which BREIN dismissed without proper legal examination.

Preventing Similar Situations

To avoid such conflicts, AI developers can take several proactive steps:

1. Use Clearly Licensed Data: Prioritize training data from public domain sources or with explicit licenses for AI training use.

2. Develop Filtering Tools: Create systems to identify and remove potentially copyrighted content from training datasets.

3. Engage with Rightsholders: Proactively discuss licensing or fair use agreements with publishers and authors for using copyrighted works in AI training.

4. Support Legal Clarity: Advocate for clearer laws and regulations around copyright and AI training to provide a more stable development environment.

5. Transparency in Training: Clearly document and disclose the sources of training data used in AI models.

6. Collaborate on Ethical Guidelines: Work with industry peers to establish best practices for respecting intellectual property rights in AI development.

By taking these steps, AI developers can help mitigate legal risks while still advancing the field of artificial intelligence in an open and innovative manner.

Google Gemini’s answer:
NO

Explanation:

While BREIN's actions led to the removal of the GEITje LLM, their perspective and actions are not necessarily justifiable. Here's why:

• Fair Use and Research: The developer of GEITje cited fair use and exceptions for text and data mining for scientific purposes. These are legitimate arguments. Utilizing copyrighted material for research, training AI models, and advancing scientific understanding can be considered fair use in many jurisdictions.

• Chilling Effect on Innovation: BREIN's actions may have a chilling effect on AI development, particularly in the open-source and academic spheres. Researchers and developers may be discouraged from creating and sharing innovative AI models due to the fear of copyright infringement lawsuits. This could stifle progress in AI research and development.

• Lack of Clear Legal Precedence: The legal landscape surrounding copyright and AI is still evolving. There is a lack of clear legal precedent in many jurisdictions regarding the use of copyrighted material for training AI models.

How AI Developers Can Prevent Similar Situations:

• Use Legally Sourced Data: Prioritize the use of publicly available, open-source, or copyright-free datasets for training AI models. This minimizes the risk of copyright infringement.

• Transparency and Collaboration: Be transparent about the data sources used to train AI models. Collaborate with copyright holders to explore potential licensing agreements or data sharing arrangements.

• Engage in Dialogue: Engage in open dialogue with copyright holders, policymakers, and legal experts to develop a clear and balanced legal framework for AI training.

• Support Open-Source Initiatives: Support and contribute to open-source AI initiatives that prioritize the use of legally sourced data and promote responsible AI development.

• Develop Robust Legal Strategies: If facing copyright infringement claims, consult with legal experts to develop a robust legal defense strategy based on fair use, research exceptions, and the evolving legal landscape.

It's important to note that this is a complex issue with no easy answers. Striking a balance between protecting copyright and fostering AI innovation is crucial for the future of both industries.

DeepSeek’s answer:
Agreement with BREIN’s Perspective and Actions: NO

While BREIN’s actions are aimed at protecting copyright holders, I do not fully agree with their approach. The primary reason is that their actions, while legally justified in some cases, may stifle innovation and limit the development of open-source AI models, especially in non-commercial or scientific contexts. The developer of GEITje, Edwin Rijgersberg, created the model as a hobby and for scientific purposes, which aligns with the copyright exceptions for text and data mining under certain jurisdictions (e.g., the EU’s AI Act). However, BREIN’s pressure led to the shutdown of a non-commercial project, which could discourage other developers from contributing to the open-source AI community.

Moreover, the legal landscape around AI and copyright is still evolving, and the courts have yet to definitively rule on whether training AI models on copyrighted material constitutes fair use or infringement. BREIN’s aggressive stance may preemptively limit the potential benefits of AI development, especially in smaller, non-commercial projects that lack the resources to defend themselves legally.

How AI Developers Can Prevent This Situation:

1. Use Legally Sourced Datasets: AI developers should prioritize using datasets that are either in the public domain, licensed under permissive terms (e.g., Creative Commons), or explicitly cleared for use in AI training. This reduces the risk of copyright infringement claims.

2. Collaborate with Rightsholders: Developers can work with publishers, authors, and other rightsholders to obtain proper licenses or permissions for using copyrighted material. This could involve negotiating agreements that benefit both parties, such as revenue-sharing models or attribution requirements.

3. Implement Robust Data Filtering: Developers can implement tools to filter out copyrighted material from training datasets. While this may not be foolproof, it demonstrates a good-faith effort to respect copyright laws.

4. Advocate for Clear Legal Frameworks: AI developers and organizations should advocate for clearer legal guidelines around the use of copyrighted material in AI training. This could involve lobbying for legislation that explicitly allows for text and data mining under certain conditions, such as non-commercial or scientific use.

5. Open-Source and Transparency: By making datasets and training processes transparent, developers can build trust with the community and rightsholders. Open-source projects can also benefit from community scrutiny, which can help identify and address potential copyright issues early on.

6. Ethical AI Development: Developers should adopt ethical guidelines that prioritize respecting intellectual property rights. This includes conducting due diligence on the sources of training data and ensuring that the use of such data aligns with ethical and legal standards.

In conclusion, while BREIN’s actions are rooted in protecting copyright, their approach may have unintended consequences for innovation and open-source AI development. AI developers can take proactive steps to avoid legal conflicts by using legally sourced data, collaborating with rightsholders, and advocating for clearer legal frameworks. This balanced approach can help foster innovation while respecting intellectual property rights.