GPT-4o: The judge relies heavily on precedents within the Ninth Circuit, which consistently hold that no DMCA violation exists where the works are not identical.

Question 1 of 3 for ChatGPT-4o: Please compare the article "Judge dismisses coders’ DMCA claims against Microsoft, OpenAI and GitHub" to information about the complaint and tell me everything you know about the motivation of the judge and what he is or was expecting from the Plaintiffs.

ChatGPT-4o’s answer:
The motivation of Judge Jon S. Tigar in dismissing the coders' DMCA claims against Microsoft, OpenAI, and GitHub is rooted in several key points from his orders and expectations from the Plaintiffs, as highlighted in the documents provided.

Motivation of the Judge

1. DMCA Identicality Requirement: Judge Tigar's main motivation in dismissing the DMCA claims revolves around the "identicality" requirement of Section 1202(b) of the DMCA. He emphasizes that to state a claim under this section, Plaintiffs must demonstrate that Defendants removed or altered copyright management information (CMI) from an identical copy of a copyrighted work. In his view, Plaintiffs failed to meet this requirement because they did not identify instances where Copilot produced an identical copy of their code​.

2. Consistency with Ninth Circuit Caselaw: The judge relies heavily on precedents within the Ninth Circuit, which consistently hold that no DMCA violation exists where the works are not identical. He references cases like Kirk Kara Corp. v. W. Stone & Metal Corp. and Frost-Tsuji Architects v. Highway Inn, Inc., which require identical copies for a DMCA Section 1202(b) claim​.

3. Inadequacy of New Allegations: Despite Plaintiffs’ new allegations in their second amended complaint (SAC), which included a user-settable Copilot filter and academic research suggesting the likelihood of verbatim code output, the judge found these insufficient. He reasoned that the existence of a duplication-detection feature does not plausibly suggest that Copilot would output identical copies of Plaintiffs’ works through normal operation​.

Expectations from the Plaintiffs

1. Concrete Examples of Identical Copying: Judge Tigar expected Plaintiffs to provide specific instances where their code was output by Copilot in an identical manner. The lack of such examples was a critical factor in dismissing their DMCA claims. The Court pointed out that Plaintiffs' allegations largely described modifications rather than identical copies of their code being produced .

2. Substantial Risk of Inducing Infringement: While Plaintiffs argued that Defendants’ programs were designed to remove CMI and that this carried a substantial risk of inducing infringement, the judge required more concrete proof. The mere possibility or risk of future identical output was not enough; Plaintiffs needed to demonstrate actual instances of such output.

3. Amendments to Cure Deficiencies: Judge Tigar granted Plaintiffs leave to amend their DMCA claims previously, showing his expectation that Plaintiffs would address the deficiencies identified. However, after multiple attempts and continued failure to meet the identicality requirement, the judge dismissed the claims with prejudice, indicating his conclusion that further amendments would be futile.

Summary

Judge Tigar's dismissal of the DMCA claims was heavily influenced by the requirement for identical copies under Section 1202(b) and the Plaintiffs’ failure to meet this requirement despite amendments and new allegations. He consistently referenced Ninth Circuit caselaw supporting this requirement and expected the Plaintiffs to provide concrete examples of identical copying, which they did not. This led to the ultimate dismissal of the claims with prejudice, reflecting his determination that further amendments would not rectify the identified deficiencies​.

Question 2 of 3 for ChatGPT-4o: What can AI makers and those wanting to litigate against them learn from this situation?

ChatGPT-4o’s answer:
AI makers and those planning to litigate against them can derive several important lessons from this case. Here are some key takeaways:

For AI Makers:

1. Ensure Compliance with Copyright Laws: AI companies should carefully design their models to avoid using copyrighted material without proper authorization. They should implement robust measures to track and manage the sources of their training data to prevent inadvertent infringement.

2. Transparency in Data Usage: Being transparent about the sources of training data and obtaining necessary permissions can protect AI makers from legal disputes. Clear documentation and adherence to licenses are crucial.

3. Implementing Filtering Mechanisms: Features like GitHub Copilot's duplication-detection filter, which can block suggestions that match public code, can be a useful defense mechanism. However, these features need to be effective and mandatory, not optional, to strengthen the defense against copyright claims.

4. Legal Readiness and Documentation: Maintaining detailed records of how the AI models are trained and the steps taken to comply with copyright laws can be critical in defending against legal challenges. Regular audits and legal reviews can help in staying compliant.

For Those Wanting to Litigate Against AI Makers:

1. Understand the Legal Standards: Plaintiffs need to be well-versed with the legal standards required to succeed in their claims. In the case of DMCA claims, understanding the identicality requirement was crucial. Plaintiffs must ensure their claims meet the specific legal criteria set forth by relevant laws and precedents.

2. Provide Concrete Evidence: It's essential to provide specific examples of infringement, such as instances where the AI has produced identical copies of copyrighted work. General allegations or theoretical risks are insufficient.

3. Leverage Expert Studies and Reports: Academic research and expert reports can support claims, but they must directly relate to the case and demonstrate the likelihood of infringement clearly and convincingly. Plaintiffs should ensure the studies they rely on are applicable to their specific situation.

4. Amendments and Persistence: When initial claims are dismissed, plaintiffs should carefully amend their complaints to address the deficiencies identified by the court. Detailed and precise amendments can be critical in keeping the case alive.

5. Focus on Clear Violations: Litigants should focus on clear and undeniable violations. In this case, the failure was due to the inability to show identical copying. Plaintiffs need to pinpoint clear, direct violations of copyright or other relevant laws.

Summary

For AI makers, this situation underscores the importance of compliance, transparency, and proactive legal strategies. For litigants, it highlights the necessity of understanding legal requirements, providing concrete evidence, and carefully constructing their cases. Both sides can learn from this case to better navigate the complex legal landscape surrounding AI and intellectual property.


Question 3 of 3 for ChatGPT-4o: Do you agree with the views of the Judge or is there room for criticism or improvement?

ChatGPT-4o’s answer:
The views of Judge Jon S. Tigar in the case against Microsoft, OpenAI, and GitHub regarding the DMCA claims provide a legal perspective grounded in current jurisprudence, particularly the requirement for identical copying under Section 1202(b) of the DMCA. However, like any legal decision, it can be subject to both agreement and criticism. Here are some points to consider:

Agreement with the Judge's Views

1. Adherence to Legal Precedent: Judge Tigar's decision aligns with established Ninth Circuit precedents, which require identical copying for a DMCA Section 1202(b) claim. This consistency upholds the rule of law and provides predictability in legal interpretations.

2. Requirement for Concrete Evidence: The judge's insistence on concrete examples of identical copying is reasonable. This requirement ensures that claims are substantiated with clear evidence, preventing frivolous or speculative lawsuits.

3. Fair Opportunity for Amendment: The plaintiffs were given multiple opportunities to amend their complaints, reflecting the judge’s willingness to allow them to rectify deficiencies. This shows a balanced approach, providing plaintiffs with a fair chance to make their case.

Room for Criticism or Improvement

1. Rigidity of Identicality Requirement: The stringent requirement for identical copying could be seen as overly rigid, potentially overlooking instances where substantial portions of code are copied or where the spirit of copyright protection is violated. There may be room for interpreting the DMCA more flexibly to cover near-identical or substantially similar copies.

2. Consideration of Technological Context: The ruling might not fully consider the nuances of how modern AI models operate. AI outputs might not be identical but still could infringe on the essence of copyrighted works. A more nuanced approach considering the technological context could provide better protection for intellectual property.

3. Impact on Open Source and Innovation: The decision could be perceived as favoring large tech companies, potentially stifling smaller developers and innovators who rely on open-source licenses. Balancing the interests of protecting open-source contributions with fostering AI innovation could be improved.

4. Future Implications for AI Development: The case highlights the need for clearer guidelines and regulations regarding the use of copyrighted material in AI training. This case could prompt legislative or judicial review to adapt copyright laws to the realities of AI and machine learning technologies.

Balanced Perspective

While the judge's decision is legally sound and follows existing precedents, it also exposes areas where the law might need to evolve. Both the strict interpretation of identicality and the broader implications for technology and innovation suggest that there is room for future legal and policy developments.

Summary

In summary, while Judge Tigar's ruling is legally justified and adheres to precedent, it may benefit from reconsideration or criticism in light of the evolving technological landscape and the broader implications for copyright law in the era of AI. Both sides of the debate offer valid points, and this case could serve as a catalyst for more nuanced and adaptive legal frameworks in the future.