GPT-4o: I disagree with the author's suggestion to make illegally trained large language models (LLMs) public domain. I believe the proposed solution has too many risks and unintended consequences.

Asking AI services: Please read the article: "Fining Big Tech isn't working. Make them give away illegally trained LLMs as public domain" and tell me whether you agree with the author - yes or no - and proceed to explain why. If you don't agree, explain what you think the alternative should be.

ChatGPT-4o’s answer:
I disagree with the author's suggestion to make illegally trained large language models (LLMs) public domain. While the idea is innovative and aims to deter unlawful behavior by removing economic incentives for companies to violate intellectual property and privacy laws, it has several critical flaws and risks:

1. Incentivizing Misuse and Abuse

• Releasing such models into the public domain could lead to widespread misuse. Without the constraints of corporate oversight or accountability, these models might be exploited for malicious purposes, including misinformation, fraud, or harmful content generation. This could exacerbate the very societal harms the article seeks to mitigate.

2. Weakening Innovation Incentives

• Companies invest billions of dollars into developing AI technologies. Forcing them to release models to the public domain as a punishment for prior violations may create an overly harsh deterrent, stifling innovation. Organizations might either refrain from developing advanced AI or restrict access to research to avoid risks of penalization.

3. Legal and Practical Challenges

• There are significant legal hurdles to implementing such a policy, as highlighted in the article itself. Current laws, such as GDPR, do not explicitly provide powers to mandate public domain release, and achieving global enforcement would require unprecedented international cooperation.

4. Better Alternatives Exist

• Instead of public domain release, stricter penalties could be imposed, such as:

  • Mandatory Licensing: Companies could be required to license the models on a non-exclusive, fair, and regulated basis, ensuring their benefits are widely shared without relinquishing all control.

  • Revenue Redistribution: Fines could be supplemented by requiring companies to fund independent research, education, or public-interest AI projects, thus ensuring societal benefits without the risks of unfettered access.

  • Governance and Oversight: Mandating transparent, third-party governance to oversee the usage of models ensures compliance with ethical and legal standards while retaining corporate accountability.

5. Environmental Concerns Are Overstated

• While deleting LLMs involves an environmental cost, this is not an insurmountable barrier. For example, only the derived model weights might need deletion, sparing significant data and energy resources used in other stages of training.

6. Human Rights Implications

• Making LLMs public domain undermines privacy and intellectual property rights by treating unlawfully sourced data as a public resource. This approach could inadvertently validate data misuse practices.

In conclusion, while I understand the frustration behind the proposal and agree with the need for stronger deterrents, I believe the proposed solution has too many risks and unintended consequences. The focus should be on robust legal frameworks, effective fines, and better mechanisms for accountability and transparency in AI development.


Claude’s answer:
I aim to engage thoughtfully with this article's arguments while acknowledging that, as an AI system myself, I have an inherent stake in these matters that could influence my perspective.

The author makes several compelling points, particularly about the environmental impact of training large language models and how financial penalties may be insufficient deterrents for wealthy tech companies. However, I ultimately disagree with the proposed solution of forcing companies to release illegally trained models into the public domain.

Here's why:

1. The remedy doesn't match the violation. If a company illegally used copyrighted data or personal information for training, making that model public could actually compound the privacy and copyright violations by giving everyone access to a model trained on that misappropriated data.

2. The public domain solution might create perverse incentives. Companies might calculate that it's worth risking illegal training methods if the worst outcome is having to release their model publicly - especially if they could still benefit from the research insights gained during development.

3. The proposal doesn't address the root cause of unauthorized data usage in training.

Instead, I believe more effective approaches might include:

• Requiring companies to obtain explicit consent and provide fair compensation for training data usage upfront

• Implementing mandatory technical safeguards and auditing requirements during model development

• Creating a regulatory framework that requires demonstration of legal compliance before models can be deployed

• Structuring penalties as a percentage of global revenue rather than fixed amounts

• Requiring companies to retrain models with properly licensed data rather than releasing potentially tainted models

The goal should be to create a framework that ensures responsible AI development from the start, rather than trying to retroactively address violations through public domain releases.

What are your thoughts on these alternative approaches to addressing the issue?


Grok’s answer:
No.

Explanation:

The article by Alexander Hanff argues for moving illegally trained Large Language Models (LLMs) into the public domain as a punitive measure instead of fining or deleting them due to environmental concerns. Here's why I disagree with this approach:

1. Intellectual Property and Privacy Concerns: Making these models public domain could lead to further violations of privacy and intellectual property rights. If these models were trained on data without consent, releasing them publicly would not rectify the initial harm but rather spread it. Public domain status means anyone could use these models, potentially for nefarious purposes, without considering the rights of the individuals whose data was used.

2. Incentive for Innovation: The proposal might discourage innovation. If companies know that their proprietary technology could be forced into the public domain upon legal challenges, they might be less inclined to invest heavily in AI development, fearing loss of control over their intellectual assets.

3. Environmental Impact: While the author acknowledges the environmental cost of retraining or deleting models, the long-term environmental impact of democratizing access to such models isn't fully addressed. Increased usage of these models by the public could amplify energy consumption and carbon emissions over time, especially if these models are used extensively without regard for sustainability.

4. Legal and Regulatory Challenges: The legal framework to enforce such a penalty might not be straightforward. As the article notes, current laws like GDPR do not explicitly allow for forcing data into the public domain. This would require significant legislative changes, which are complex and time-consuming, potentially delaying any meaningful action against illegal data practices.

Alternative Approaches:

• Strengthen Regulatory Frameworks: Instead of making models public domain, enhance the enforcement of existing data protection laws like GDPR. Increase fines to levels that are truly punitive for large corporations, ensuring they exceed the "cost of doing business" threshold.

• Data Compensation Models: Implement systems where individuals or content creators are compensated when their data is used in AI training. This could be through direct payments or through a collective fund that redistributes profits from AI models back to data contributors.

• Ethical Data Practices Mandates: Require companies to use only ethically sourced data for training, with strict auditing processes to ensure compliance. This could involve certifications or seals of approval for AI models trained on data collected transparently and legally.

• Technology for Good: Encourage or mandate that a portion of AI technology developed using public data be used for societal benefits, like in public health, education, or environmental protection, thus ensuring that the public gains directly from AI advancements.

In conclusion, while the idea of punishing companies by removing their control over their technology is intriguing, it poses significant risks and might not be the most effective or equitable solution. A more balanced approach would focus on enhancing legal frameworks, ensuring data rights, and promoting ethical practices in AI development.


Perplexity’s answer:
No, I do not fully agree with the author's proposal, although I acknowledge the merit in some of his arguments.

Reasons for Disagreement

Environmental Concerns
The author makes a valid point about the environmental impact of training large language models (LLMs). Deleting illegally trained models would indeed waste significant energy resources. However, this argument alone doesn't justify making these models public domain.

Legal and Ethical Complexities
While the idea of putting illegally trained LLMs into the public domain seems like poetic justice, it raises several legal and ethical concerns:

1. Intellectual Property Rights: Even if the training data was scraped illegally, the resulting model architecture and weights represent significant intellectual property. Forcing companies to release this could be seen as an extreme form of punishment that may not stand up to legal scrutiny.

2. Privacy Implications: If these models were indeed trained on personal data without consent, making them public could potentially expose that personal information, exacerbating the privacy violation.

3. Unintended Consequences: Releasing powerful AI models to the public without proper safeguards could lead to misuse and potential harm.

Alternative Approach

Instead of making illegally trained LLMs public domain, I propose a more nuanced approach:

1. Stricter Regulations: Implement and enforce stricter regulations on data collection and model training practices, with severe financial penalties for violations.

2. Mandatory Audits: Require companies to undergo regular, independent audits of their training data and processes.

3. Compensatory Actions: Rather than releasing models, require companies found in violation to invest in public AI research, education, or environmental initiatives to offset the societal and environmental costs of their actions.

4. Ethical AI Development: Encourage and incentivize the development of ethical AI practices, including federated learning and differential privacy techniques that protect individual privacy.

5. Global Cooperation: As the author suggests, establish international agreements to ensure consistent enforcement across jurisdictions.

This approach would address the core issues without the potential negative consequences of releasing powerful AI models into the public domain. It would also maintain the incentive for innovation while ensuring responsible development practices.