The First Draft General-Purpose AI Code of Practice outlines several consequences for AI makers, emphasizing responsibilities and accountability in developing and deploying general-purpose AI models.

Question 1 of 3 for ChatGPT-4o: Please read the First Draft General-Purpose AI Code of Practice and tell me what it says

ChatGPT-4o’s answer:
The First Draft General-Purpose AI Code of Practice, as outlined in the provided documents, focuses on guiding the development and regulation of general-purpose AI models in compliance with the EU AI Act. Below is a summary of its main points:

Key Sections and Measures:

1. Transparency and Documentation:

   • Providers must maintain detailed documentation for their models, including their training and testing processes.

   • Transparency extends to both the AI Office and downstream users, covering aspects like model capabilities, architecture, data sources, and energy consumption.

2. Copyright Compliance:

   • Providers must implement a copyright policy ensuring compliance with EU copyright laws, including handling opt-outs indicated by rights reservations.

   • Reasonable due diligence and downstream measures are required to prevent models from generating outputs that infringe on copyright.

3. Systemic Risk Assessment:

   • A taxonomy of systemic risks is included, addressing areas like cyber threats, manipulation, large-scale discrimination, and autonomous decision-making failures.

   • Measures emphasize identifying, analyzing, and mitigating risks throughout the AI lifecycle.

4. Safety and Security:

   • Providers are required to adopt a Safety and Security Framework (SSF) detailing risk mitigation policies.

   • This includes both safety mitigations (e.g., behavioral safeguards) and security measures (e.g., access control and protection of model weights).

5. Governance and Oversight:

   • Providers must ensure systemic risk ownership at executive and board levels.

   • They must document and report serious incidents and provide transparency about their compliance measures.

6. Copyright-Specific Recommendations:

   • Avoid overfitting models to prevent memorization of copyrighted content.

   • Collaborate on standardizing machine-readable opt-outs for copyright reservations.

   • No crawling of piracy websites.

7. Open Questions:

   • The draft invites stakeholders to provide feedback on areas like systemic risk definitions, mitigation strategies, and evaluation methods.

Objectives:

• Align with EU principles and international approaches while ensuring proportionality in measures based on risk levels and provider size.

• Support innovation and AI safety through collaborative efforts, transparency, and adaptability to technological advancements.

Implementation:

• The draft is a preliminary version, seeking further refinement based on feedback. It highlights the importance of cooperation among stakeholders to finalize a robust, future-proof framework.

Question 2 of 3 for ChatGPT-4o: What are the consequences for AI makers?

ChatGPT-4o’s answer:
The First Draft General-Purpose AI Code of Practice outlines several consequencesfor AI makers, emphasizing responsibilities and accountability in developing and deploying general-purpose AI models. These consequences span regulatory, operational, and reputational dimensions:

1. Compliance Obligations

AI makers are required to:

• Adopt robust documentation practices: Non-compliance with transparency requirements (e.g., maintaining technical documentation, disclosure of data sources) could result in penalties or regulatory scrutiny.

• Implement a copyright compliance policy: Failure to comply with copyright laws (e.g., handling opt-outs, avoiding the use of pirated content) can lead to legal challenges from rightsholders or collective management bodies.

• Follow systemic risk mitigation measures: AI makers must assess, document, and mitigate systemic risks. Non-compliance could lead to restrictions on deploying or further developing their models.

2. Liability for Systemic Risks

AI makers bear legal and operational responsibility for:

• Harm caused by their models: If their models contribute to risks like disinformation, discrimination, or cyber vulnerabilities, they could face lawsuits or fines.

• Post-market monitoring: AI makers are required to track their models’ impacts during and after deployment, ensuring continuous risk mitigation. Neglecting this can lead to reputational damage and increased liability.

3. Greater Oversight and Reporting

• Mandatory incident reporting: Providers must report serious incidents (e.g., misuse, harmful outputs) to the AI Office. Failure to do so could result in sanctions or loss of trust from regulators and stakeholders.

• Inspections and audits: Regulators may demand access to documentation or conduct audits, and discrepancies could result in penalties or restricted operations.

4. Reputational Risks

• Transparency requirements: The draft mandates public disclosure of compliance efforts. Non-transparency or evidence of inadequate measures could harm the maker's reputation among customers, partners, and the public.

• Collaboration failures: Refusal or inability to collaborate on setting industry standards (e.g., for copyright opt-outs or systemic risk mitigation) may isolate AI makers from industry partnerships and regulatory goodwill.

5. Adaptation Costs

• Compliance with evolving standards: AI makers face ongoing costs to align with new standards and best practices, including:

  • Updating safety frameworks.

  • Reassessing risks.

  • Adopting state-of-the-art technologies.

• Cybersecurity investments: Securing models and infrastructure against risks like model theft or unauthorized use is a mandatory and potentially expensive obligation.

6. Restrictions on Deployment

• Models that are deemed to pose intolerable systemic risks or fail compliance checks could:

  • Be removed from the market.

  • Require additional safeguards or modifications before re-deployment.

7. Opportunity Costs

• Focus on safety and compliance may slow innovation: Compliance requirements (e.g., thorough evaluations, collaboration, documentation) might limit the pace of development and deployment.

• Market access: Failure to meet EU standards could restrict the ability of non-compliant AI makers to operate in the EU market.

Conclusion

The draft imposes strict requirements for safety, transparency, copyright compliance, and systemic risk mitigation. AI makers who fail to meet these standards face legal, financial, and reputational consequences. However, those who proactively comply may benefit from reduced risks, enhanced trust, and smoother market access.


Question 3 of 3 for ChatGPT-4o: What are the consequences for creators, rights holders and IP owners?

ChatGPT-4o’s answer:
The First Draft General-Purpose AI Code of Practice outlines significant consequences—both challenges and opportunities—for creators, rights holders, and intellectual property (IP) owners. These stem from how AI developers are expected to interact with copyrighted works and other protected content:

1. Increased Control Over Content Usage

• Stronger Copyright Protections:

  • AI developers are required to implement policies that respect copyright laws, including honoring opt-outs and rights reservations in machine-readable formats (e.g., robots.txt files or metadata).

  • Creators and rights holders can expect better mechanisms to prevent unauthorized use of their content in AI model training and outputs.

• Transparency Measures:

  • AI makers must disclose the data sources used for training, testing, and validation, allowing creators to verify whether their works have been included without permission.

2. Mechanisms for Enforcement

• Complaint Handling:

  • Rights holders can lodge complaints about suspected unauthorized use of their works. AI providers are encouraged to set up a single point of contact for this purpose.

• Regulatory Oversight:

  • The AI Office can monitor compliance, including whether AI developers have obtained proper licenses for copyrighted content.

3. Reduced Risk of IP Infringement

• Downstream Obligations:

  • AI developers must implement safeguards to prevent downstream users from creating outputs identical or recognizably similar to protected works. This reduces the risk of derivative works infringing on IP rights.

• Avoidance of Overfitting:

  • By discouraging models from memorizing and reproducing specific works, the Code helps prevent unintended reproduction of copyrighted material.

4. New Challenges and Responsibilities

• Proving Rights Reservations:

  • Rights holders may need to actively reserve their rights using tools like metadata or robots.txt. Content not explicitly reserved may be at risk of being used without explicit authorization.

• Enforcement Burden:

  • While the draft provides mechanisms for lodging complaints, the responsibility to monitor and report potential misuse still largely falls on rights holders, which can be resource-intensive.

5. Increased Licensing Opportunities

• Market for Authorized Data:

  • The draft emphasizes lawful access to copyrighted content, potentially increasing demand for licensed datasets. Creators and rights holders could monetize their works by licensing them to AI developers.

• Collective Bargaining:

  • The framework encourages collective management organizations to represent creators, enabling them to negotiate licenses or enforce rights on behalf of their members.

6. Potential Loss of Control Without Vigilance

• Content Usage Under Exceptions:

  • Under the text and data mining (TDM) exceptions in EU copyright law, works not explicitly reserved may be used for AI training without additional licensing. This means rights holders must take proactive steps to reserve their rights.

• Challenges With Detection:

  • Despite transparency requirements, detecting and proving unauthorized use of copyrighted works in massive datasets remains challenging.

7. Opportunities for Standardization and Collaboration

• Participation in Standards Development:

  • Rights holders are invited to collaborate in creating interoperable standards for machine-readable rights reservations. This could lead to more uniform protections across industries.

• Improved Complaint Handling and Transparency:

  • With mechanisms in place to handle complaints and ensure transparency about crawler behavior and data usage, creators gain more avenues to assert their rights.

8. Reputational Risks

• Misattribution Risks:

  • If AI outputs mimic protected works, rights holders may face confusion or reputational harm if the public misattributes AI-generated content to them.

• Brand Dilution:

  • Unauthorized use of distinctive works could dilute the brand or artistic identity of creators, especially if the AI outputs are of lower quality.

Conclusion

The draft creates a framework that strengthens the legal and procedural tools available to creators, rights holders, and IP owners, aiming to protect their works and ensure fair compensation. However, these groups must remain vigilant and proactive in reserving rights and engaging with enforcement mechanisms to fully benefit from these protections.