GPT-4o: AI developers will need to ensure that their training data complies with copyright laws, especially regarding the transient/incidental copies and TDM exceptions.

Question for ChatGPT-4o: Please read the LinkedIn posts from Matt Hervey and Mirko Brüß and tell me what the key challenges are and explain how they can affect AI makers and AI users alike.

ChatGPT-4o’s answer:
The key challenges discussed in the LinkedIn posts by Matt Hervey and Mirko Brüß revolve around legal and technical issues concerning copyright and the application of text-and-data mining (TDM) exceptions in AI model training. These challenges can significantly affect both AI makers and users in several ways:

Key Challenges

1. Legal Uncertainty Regarding Transient and Incidental Copies:

   • The German court's decision that downloading an image from a stock photo site (even in low resolution and with a watermark) does not constitute a temporary act of reproduction under Section 44a UrhG indicates strict interpretation of what constitutes transient or incidental copies.

   • This implies that AI developers cannot rely on these exceptions for data used in training models, raising potential legal risks.

2. Application of TDM Exceptions:

   • The court's preliminary finding on the TDM exception under Section 44b UrhG is pivotal. While the image was lawfully accessible, the court is inclined to accept its use for gathering correlations, patterns, and trends, which is beneficial for AI development.

   • However, the debate on whether opt-outs need to be in a machine-readable format (e.g., robots.txt) or can be in human-readable text adds complexity. This uncertainty could complicate compliance for AI developers using large datasets scraped from the web.

3. Opt-out Mechanisms:

   • The court noted that the opt-out in this case was declared by a third party, not the original rights holder (Kneschke), but found it sufficient due to the contractual relationship between the photographer and the stock photo site.

   • This raises questions about the enforceability of opt-outs and whether AI developers need to verify the opt-out status of data from third-party sources, potentially adding to the burden of data management and legal compliance.

Implications for AI Makers and Users

For AI Makers:

• Increased Legal Compliance Burden:

  • AI developers will need to ensure that their training data complies with copyright laws, especially regarding the transient/incidental copies and TDM exceptions.

  • The need for clear opt-out mechanisms means additional legal and technical overhead to manage data acquisition processes and ensure compliance.

• Risk of Litigation:

  • Non-compliance with copyright laws or improper handling of opt-out data could lead to legal challenges, resulting in costly litigations or settlements.

• Impact on Data Availability:

  • The strict interpretation of copyright laws might reduce the availability of data that can be used for training AI models, potentially impacting the quality and diversity of AI outputs.

For AI Users:

• Quality and Bias of AI Models:

  • Legal constraints on data use may limit the datasets available for training, potentially affecting the comprehensiveness and accuracy of AI models.

  • Users might experience biases or gaps in AI performance due to reduced training data diversity.

• Trust and Reliability:

  • Legal controversies and non-compliance issues can undermine user trust in AI systems, especially if data provenance and compliance with copyright laws are questioned.

• Cost Implications:

  • The increased costs for AI developers to ensure legal compliance could be passed on to users, potentially increasing the cost of AI products and services.

In summary, these challenges underscore the need for AI makers to navigate complex legal landscapes diligently and for AI users to be aware of the potential limitations and risks associated with the AI systems they utilize.